Generated by All in One SEO Pro v5.0.1.1, this is an llms-full.txt file, used by LLMs to index the site. # Analyticsn Unlock Data Insights with Expert Analysts ## Posts ### [Blog](https://analyticsn.com/blog/) **Published:** April 3, 2024 **Author:** AnalyticsN **Content:** ## Blogs --- ### [Topic 1.  Data Analysts Need AI for Data Analytics](https://analyticsn.com/topic-1-data-analysts-need-ai-for-data-analytics/) **Published:** January 27, 2026 **Author:** AnalyticsN **Content:** ![Analyticsn AI for Data Analytics; Learn why AI enhances productivity, won’t replace data analysts, and how to future-proof your data career with the right skills. Introduction AI for Data Analytics and the data landscape is evolving rapidly, and Artificial Intelligence (AI) tools like ChatGPT, Google Bard, and others are transforming the way professionals work. Tasks that were once time-consuming and complex can now be completed in minutes, or even seconds, thanks to these technologies. Why AI for Data Analytics is AI for data analysts, artificial intelligence in data analytics, ChatGPT for data professionals, Google Bard for analysts, AI tools for data analysis, future of data analyst jobs, AI vs data analysts, prompt engineering for analysts, analytics workflows with AI, data analytics career skills, AI productivity tools, modern data analyst skills, AI in business analytics, data analyst competitive advantage analyticsn.com](https://analyticsn.com/wp-content/uploads/2026/01/1.-why-do-data-analysts-need-AI-1024x576.png "1. why do data analysts need AI - Analyticsn") ## **Introduction** AI for Data Analytics and the data landscape is evolving rapidly, and Artificial Intelligence (AI) tools like ChatGPT, Google Bard, and others are transforming the way professionals work. Tasks that were once time-consuming and complex can now be completed in minutes, or even seconds, thanks to these technologies. ## **Why AI for Data Analytics is a Game Changer** AI technology is still in its early stages. While its performance varies across tasks, its capabilities are improving steadily. For data professionals, the benefits of adopting AI tools are significant: - **Enhanced Efficiency:** Automate routine and repetitive tasks to focus on high-impact analytical work. - **Quality Assurance:** Use AI as a “code buddy” to verify code, troubleshoot issues, and refine analysis. - **Accelerated Learning:** AI tools offer instant responses to technical questions, supporting continuous learning. - **Competitive Advantage:** Staying current with AI ensures relevance and competitiveness in a rapidly evolving job market. ## **Is AI Going to Replace Data Analysts?** The concern that AI will replace data analysts is largely exaggerated. While AI can handle certain technical tasks, human analysts remain indispensable due to their intuition, context understanding, and critical thinking. What is changing, however, are the **core skills required to thrive**. ## **The AI for Data Analytics Trifecta** To succeed as a modern data analyst, professionals need a blend of: 1. **Strategic Thinking** – Understanding business challenges and identifying data-driven solutions. 2. **Technical Proficiency** – Working with data tools, coding languages, and AI technologies. 3. **Communication Skills** – Telling compelling stories with data and advocating for actionable insights. AI tools can significantly assist with **technical proficiency**, helping analysts bridge skill gaps and work more efficiently. However, **strategic thinking and communication** remain uniquely human strengths that AI cannot replicate. ## **The Future of Data Careers** The role of the data analyst will continue to evolve. Success depends on building a strong foundation in core competencies while staying adaptable to new technologies. Just as spreadsheets, the internet, Tableau, and Power BI became essential in previous decades, AI [tools are now a critical addition to the data](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/) professional’s toolkit. ## **Conclusion: Take Action Now** AI will not take your job—but someone who knows how to use AI might. Embrace this shift by: - Learning to integrate AI tools into your workflow. - Strengthening foundational skills in strategic thinking and communication. - Staying agile and open to future technological changes. By doing so, you’ll position yourself as an invaluable asset to any organization and ensure long-term success in your data career. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI for data analysts, AI in business analytics, AI productivity tools, AI tools for data analysis, AI vs data analysts, analytics workflows with AI, artificial intelligence in data analytics, ChatGPT for data professionals, data analyst competitive advantage, data analytics career skills, future of data analyst jobs, Google Bard for analysts, modern data analyst skills, prompt engineering for analysts --- ### [Topic 2. AI in Analytics: A Structured Guide to High-Impact Use Cases](https://analyticsn.com/topic-2-ai-in-analytics-workflow-a-structured-guide-to-high-impact-use-cases/) **Published:** January 29, 2026 **Author:** AnalyticsN **Content:** ![Analyticsn Discover the top AI in Analytics use cases for data analysts, code generation, query optimization, automation, visualization tips, debugging and which are most mature vs. still evolving AI in Analytics is already transforming the analytics workflow in practical, high-impact ways: at the top of the list are code and formula generation, automation of repetitive tasks, and query/formula optimization—these deliver the biggest productivity gains today—followed closely by data-visualization advice and step-by-step tutorial generation that speed learning and storytelling. Equally useful (and often immediately AI for analytics, code generation AI, query optimization, data visualization tips, automate analytics workflows](https://analyticsn.com/wp-content/uploads/2026/01/2.-AI-in-analytics-workflow-1024x576.png "2. AI in analytics workflow - Analyticsn")AI in Analytics is already transforming the analytics workflow in practical, high-impact ways: at the top of the list are **code and formula generation**, **automation of repetitive tasks**, and **query/formula optimization**—these deliver the biggest productivity gains today—followed closely by **data-visualization advice** and **step-by-step tutorial generation** that speed learning and storytelling. Equally useful (and often immediately available) are AI-powered **debugging and troubleshooting**, **commenting and cleaning long code**, and **generating realistic sample data** for testing. Less mature (results vary) are complex end-to-end research tasks and deep conceptual tutoring where output quality depends heavily on prompts and tool capability. Watch the walkthroughs for Excel, Google Sheets, Power BI, SQL, and Python to see these use cases in action, and start practicing now so you can ride improvements in model accuracy as they arrive. ## Introduction Artificial intelligence has become a practical companion in modern analytics workflows. Rather than replacing analysts, AI enhances speed, clarity, and efficiency across everyday tasks—from writing code to improving data storytelling. Below is a structured overview of the most common and powerful AI use cases that analysts are already applying in real work environments. ## 1. Code and Formula Generation One of the strongest and most mature use cases is using AI to generate code, queries, or spreadsheet formulas. Analysts can quickly create SQL queries, Python scripts, DAX measures, or Excel formulas, reducing development time and lowering the risk of syntax errors. ## 2. AI in Analytics as a Learning Tutor AI can act as an on-demand tutor by explaining technical concepts step by step and asking guiding questions. This is especially valuable when learning new tools, languages, or analytical methods, helping analysts build understanding without interrupting their workflow. ## 3. Debugging and Troubleshooting AI is widely used to identify errors in code, queries, or formulas. By analyzing logic and syntax, it can suggest fixes, explain why an error occurs, and offer alternative approaches—making troubleshooting faster and less frustrating. ## 4. Code Commenting and Readability For long or complex scripts, AI can automatically add comments and explanations. This improves readability, makes code more human-friendly, and helps teams collaborate more effectively by documenting logic clearly. ## 5. Query and Performance Optimization AI in Analytics can review existing queries or formulas and suggest optimizations for better performance. This includes reducing redundancy, improving efficiency, and ensuring best practices—particularly useful in SQL, Power BI, and large datasets. ## 6. Step-by-Step Tutorials and Walkthroughs Another high-value use case is generating structured, step-by-step tutorials. Analysts can request guided walkthroughs for tasks in Excel, Google Sheets, Power BI, SQL, or Python, making complex processes easier to follow and replicate. ## 7. Data Visualization and Storytelling Support AI in Analytics can recommend visualization types, layout improvements, and storytelling techniques. These [tips help analysts communicate insights more clearly,](https://analyticsn.com/?p=1551) highlighting patterns, trends, and outliers in a way that resonates with stakeholders. ## 8. Automation of Repetitive Tasks AI is increasingly used to automate routine [analytical tasks such as data](https://analyticsn.com/topic-1-data-analysts-need-ai-for-data-analytics/) cleaning, report generation, and recurring calculations. Automation frees up time for higher-value analysis and strategic thinking. ## 9. Research and Technical Exploration Analysts also rely on AI to research specific technical topics, [compare tools,](https://analyticsn.com/understanding-paired-sample-test-a-key-tool-for-comparative-analysis/) or explore methods. While results may vary depending on the complexity of the topic, this use case is improving rapidly. ## 10. Sample Data Generation AI in Analytics can generate realistic [sample datasets for testing,](https://analyticsn.com/understanding-paired-sample-test-a-key-tool-for-comparative-analysis/) training, or demonstrations. This is particularly useful when real data is unavailable, sensitive, or incomplete. ## Maturity and Future Outlook Some AI use cases—such as code generation, debugging, and automation—are already highly reliable and deliver immediate value. Others, including advanced research and deep conceptual tutoring, still show variable results but are improving quickly. Learning to use these tools now ensures analysts stay aligned with technological progress and are ready to maximize their effectiveness as AI capabilities continue to evolve. ## Conclusion AI is no longer experimental in analytics—it is a practical productivity accelerator. By adopting these use cases today, analysts can work faster, communicate better, and stay ahead as AI tools continue to advance across platforms like Excel, Google Sheets, Power BI, SQL, and Python. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI for analytics, AI in Analytics, AnalyticsN, analyticsn.com, automate analytics workflows, code generation AI, data visualization tips, query optimization --- ### [Topic 3. From Artificial Intelligence to Deep Learning](https://analyticsn.com/topic-3-from-artificial-intelligence-to-deep-learning/) **Published:** February 3, 2026 **Author:** AnalyticsN **Excerpt:** Explore the world of artificial intelligence, where machines imitate human thought and perform tasks like decision-making and language understanding. Machine learning drives AI, enabling systems to improve using large data sets. Discover deep learning, modeled after the human brain, and the key difference between weak AI and strong AI. As data availability increases, these technologies are expected to gain power. Join us in understanding the complexities of AI and its impact on the future. **Content:** ![Analyticsn Understand the difference between weak and strong AI, the black box challenge, and the statistical roots that power AI systems. Explore the world of artificial intelligence, where machines imitate human thought and perform tasks like decision-making and language understanding. Machine learning drives AI, enabling systems to improve using large data sets. Discover deep learning, modeled after the human brain, and the key difference between weak AI and strong AI. As data availability increases, these technologies are expected to gain power. Join us in understanding the complexities of AI and its impact on the future.](https://analyticsn.com/wp-content/uploads/2026/01/3-from-artificial-intelligence-to-deep-learning-1024x576.png "3 from artificial intelligence to deep learning - Analyticsn")# From Artificial Intelligence to Deep Learning: Understanding the AI Landscape ## Introduction Before exploring specific AI tools such as ChatGPT or Google Bard, it is important to understand the broader artificial intelligence landscape. AI is not a single technology but a layered ecosystem of concepts and methods that build on one another. Understanding these layers helps clarify what today’s AI systems can do, where their limitations lie, and why their growth has accelerated so rapidly. ## What Is Artificial Intelligence? Artificial intelligence is an umbrella term describing machines and computer systems designed to mimic human intelligence. These systems perform tasks such as decision-making, image recognition, natural language processing, and autonomous navigation. AI focuses on replicating *outcomes* of human intelligence rather than human thinking itself. ## Machine Learning: The Core Engine of AI Within artificial intelligence lies **machine learning**, which provides the models that act as the “brains” of AI systems. Machine learning enables computers to learn patterns from data with minimal human instruction and improve performance as they are exposed to more data. The explosive growth of machine learning over recent decades is closely tied to the rapid expansion of digital data generated by the internet, mobile devices, and the Internet of Things. ## Deep Learning: Mimicking the Human Brain Nested within machine learning is **deep learning**, a highly complex family of algorithms inspired by the structure of the human brain. Deep learning models use multi-layered [neural networks](https://analyticsn.com/?p=1540) to learn patterns almost entirely without human intervention. These [models thrive on large](https://analyticsn.com/?p=1538) datasets and have driven major advances in speech recognition, image analysis, and language understanding . Large language models such as ChatGPT and Google Bard fall into this category, relying on deep learning architectures to generate human-like text and responses. ## Why Data Fueled the AI Explosion A key reason for the recent surge in AI capabilities is data availability. Modern technologies continuously generate massive volumes of data, enabling machine learning and deep learning models to achieve levels of accuracy that were previously impossible. More [data allows these models](https://analyticsn.com/our-services/amos-experts-for-data-modeling/) to refine predictions, detect subtle patterns, and generalize more effectively. ## Weak AI vs. Strong AI Current AI systems fall under **weak AI**, meaning they are designed to perform specific tasks. For example, a [language model](https://analyticsn.com/?p=1538) can explain how to drive a car but cannot physically drive one. **Strong AI**, also known as [artificial general intelligence,](https://analyticsn.com/topic-8-hidden-pitfalls-of-artificial-intelligence-what-users-must-know/) would be capable of learning and performing any task a human can, independently and across domains. While often depicted in science fiction, no strong AI systems currently exist, though many experts believe they may emerge in the future. ## AI, Machine Learning, and Statistics Both machine learning and deep learning are deeply rooted in statistical principles. However, they differ philosophically from traditional statistics. Statistics focuses on understanding and explaining relationships between variables, while machine learning prioritizes predictive accuracy. As a result, machine learning models often sacrifice interpretability for performance, enabling far greater complexity than classical statistical methods . ## The Black Box Challenge Because deep learning models do not emphasize explainability, they are often described as **black box models**. Analysts can observe inputs and outputs, but the internal decision-making process remains opaque. These [models may capture millions or billions of subtle data](https://analyticsn.com/5-data-modeling-levels-and-techniques/) patterns that are difficult—or impossible—for humans to fully interpret. ## Conclusion Understanding the AI landscape requires recognizing the layered relationship between artificial intelligence, machine learning, and deep learning. Today’s systems excel at narrow, data-driven tasks and prioritize accuracy over interpretability. As [data availability and model](https://analyticsn.com/our-services/amos-experts-for-data-modeling/) sophistication continue to grow, these technologies will become even more powerful—making foundational knowledge of AI essential for analysts, researchers, and decision-makers alike. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIHZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGcgY2xpcC1wYXRoPSJ1cmwoI2NsaXAwXzM0M185OTUpIj4KPHBhdGggZD0iTTE0LjgxNTYgMEgxLjE4MTI1QzAuNTI4MTI1IDAgMCAwLjUxNTYyNSAwIDEuMTUzMTNWMTQuODQzOEMwIDE1LjQ4MTMgMC41MjgxMjUgMTYgMS4xODEyNSAxNkgxNC44MTU2QzE1LjQ2ODggMTYgMTYgMTUuNDgxMyAxNiAxNC44NDY5VjEuMTUzMTNDMTYgMC41MTU2MjUgMTUuNDY4OCAwIDE0LjgxNTYgMFpNNC43NDY4NyAxMy42MzQ0SDIuMzcxODhWNS45OTY4N0g0Ljc0Njg3VjEzLjYzNDRaTTMuNTU5MzggNC45NTYyNUMyLjc5Njg4IDQuOTU2MjUgMi4xODEyNSA0LjM0MDYyIDIuMTgxMjUgMy41ODEyNUMyLjE4MTI1IDIuODIxODggMi43OTY4OCAyLjIwNjI1IDMuNTU5MzggMi4yMDYyNUM0LjMxODc1IDIuMjA2MjUgNC45MzQzNyAyLjgyMTg4IDQuOTM0MzcgMy41ODEyNUM0LjkzNDM3IDQuMzM3NSA0LjMxODc1IDQuOTU2MjUgMy41NTkzOCA0Ljk1NjI1Wk0xMy42MzQ0IDEzLjYzNDRIMTEuMjYyNVY5LjkyMTg4QzExLjI2MjUgOS4wMzc1IDExLjI0NjkgNy44OTY4NyAxMC4wMjgxIDcuODk2ODdDOC43OTM3NSA3Ljg5Njg3IDguNjA2MjUgOC44NjI1IDguNjA2MjUgOS44NTkzOFYxMy42MzQ0SDYuMjM3NVY1Ljk5Njg3SDguNTEyNVY3LjA0MDYzSDguNTQzNzVDOC44NTkzNyA2LjQ0MDYzIDkuNjM0MzggNS44MDYyNSAxMC43ODc1IDUuODA2MjVDMTMuMTkwNiA1LjgwNjI1IDEzLjYzNDQgNy4zODc1IDEzLjYzNDQgOS40NDM3NVYxMy42MzQ0VjEzLjYzNDRaIiBmaWxsPSIjNDM0OTYwIi8+CjwvZz4KPGRlZnM+CjxjbGlwUGF0aCBpZD0iY2xpcDBfMzQzXzk5NSI+CjxyZWN0IHdpZHRoPSIxNiIgaGVpZ2h0PSIxNiIgZmlsbD0id2hpdGUiLz4KPC9jbGlwUGF0aD4KPC9kZWZzPgo8L3N2Zz4K) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI black box models, AI fundamentals, AI vs machine learning, AnalyticsN, artificial general intelligence, Artificial intelligence landscape, ChatGPT and Google Bard, data-driven AI systems, deep learning explained, large language models, machine learning vs deep learning, neural networks, statistics vs machine learning, understanding artificial intelligence, weak AI vs strong AI --- ### [Topic 4 How Generative AI Like ChatGPT Really Work](https://analyticsn.com/topic-4-how-generative-ai-like-chatgpt-really-work/) **Published:** February 5, 2026 **Author:** AnalyticsN **Excerpt:** Generative AI is changing how we use technology, enabling machines to produce original content, like text and images, based on user prompts. Large language models (LLMs) like ChatGPT create text that mimics human writing by examining large amounts of data. How do they operate? Learn about the processes that lead to their clear responses, the importance of transformers and self-attention, and what makes today’s LLMs effective. Explore the future of generative AI and machine learning! **Content:** ![Analyticsn Generative AI is changing how we use technology, enabling machines to produce original content, like text and images, based on user prompts. Large language models (LLMs) like ChatGPT create text that mimics human writing by examining large amounts of data. How do they operate? Learn about the processes that lead to their clear responses, the importance of transformers and self-attention, and what makes today’s LLMs effective. Explore the future of generative AI and machine learning! Generative AI is changing how we use technology, enabling machines to produce original content, like text and images, based on user prompts. Large language models (LLMs) like ChatGPT create text that mimics human writing by examining large amounts of data. How do they operate? Learn about the processes that lead to their clear responses, the importance of transformers and self-attention, and what makes today’s LLMs effective. Explore the future of generative AI and machine learning!](https://analyticsn.com/wp-content/uploads/2026/01/4-how-tools-like-chatgpt-and-bard-really-work-1024x576.png "4 how tools like chatgpt and bard really work - Analyticsn")# Topic 4 How Tools Like ChatGPT Really Work: Generative AI and Large Language Models ## Introduction Generative AI represents one of the most significant advances in modern artificial intelligence. These systems are designed not just to analyze data, but to **create original content**—including text, images, and other media—based on user prompts. Among these, large language models (LLMs) have gained the most attention for their ability to generate highly human-like text. ## What Is Generative AI? Generative AI refers to [deep learning](https://analyticsn.com/?p=1522) systems capable of producing new and original outputs rather than simply classifying or retrieving existing data. Depending on the model, generative AI can create text, images, audio, and video. Popular image-generation tools such as DALL·E and Midjourney demonstrate how generative models extend beyond text into visual creativity. ## Large Language Models (LLMs) Large language models are a specialized category of generative AI focused specifically on **text generation**. These models analyze massive volumes of written content and learn the statistical structure of language. Tools such as ChatGPT belong to this category and are designed to generate coherent, context-aware responses in natural language . ## How Language Models Generate Answers At their core, [language models](https://analyticsn.com/?p=1538) operate on **probability, not knowledge**. When prompted with a sentence like *“The capital of France is …”*, the model does not “know” the answer. Instead, it calculates which word is most likely to follow based on patterns learned from data. Because “Paris” appears overwhelmingly often in similar contexts, it receives the highest probability . This process becomes more complex with open-ended questions. When asked *“When did Paris become the capital of France?”*, the model searches for similar patterns across millions of documents, associates dates and historical references, and generates a [response that statistically fits the prompt](https://analyticsn.com/?p=1555). A degree of randomness ensures variability, which is why repeated answers may differ slightly. ## Why Modern LLMs Are So Effective Early [generative models](https://analyticsn.com/?p=1538) often produced incoherent or grammatically incorrect outputs. Modern LLMs, however, have improved dramatically because they are trained on **enormous datasets** and use advanced architectures capable of [modeling the structure—or “shape”—of language](https://analyticsn.com/?p=1538) with high precision. This allows them to closely mimic human writing style and tone. ## What Does GPT Mean? GPT stands for **Generative Pre-Trained Transformer**, and each part of the term reflects a key property of these models: - **Generative**: The model creates new text rather than copying existing content. - **Pre-trained**: The model is trained on massive datasets before being fine-tuned for specific tasks or domains. - **Transformer**: The underlying deep learning architecture that enables advanced language understanding. ## Transformers and Self-Attention Transformers, first introduced in 2017, were a breakthrough in deep learning. Their defining feature is **self-attention**, which allows the model to evaluate the importance of each word in a sequence relative to others. This enables transformers to process language in context and handle long-range dependencies more effectively than earlier models . ## Scale, Cost, and Complexity Large [language models](https://analyticsn.com/?p=1538) are among the most complex AI systems ever built. Models such as GPT-4 require billions of parameters, months of computation, and massive financial investment to train. Their scale gives them unparalleled expressive power, but also makes them expensive and resource-intensive . ## Conclusion Generative AI and [large language models](https://analyticsn.com/?p=1538) represent a major leap in machine learning, shifting AI from analysis to creation. By predicting language probabilistically and leveraging transformer-based architectures, tools like ChatGPT can generate highly convincing text. While these systems do not “understand” language in a human sense, their scale, training data, and architectural sophistication allow them to perform at a level that increasingly resembles human communication. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI content generation, AI text generation, AnalyticsN, DALL·E vs Midjourney, deep learning language models, Generative AI, generative AI examples, generative pre-trained transformer, GPT meaning, GPT-4 training cost, how ChatGPT works, large language models, LLM explained, probability-based AI, self-attention mechanism, transformer models --- ### [Topic 5 Generative AI and Large Language Models](https://analyticsn.com/topic-5-generative-ai-and-large-language-models/) **Published:** February 10, 2026 **Author:** AnalyticsN **Excerpt:** Understand generative AI and large language models, including how ChatGPT works. Learn how transformers, self-attention, and probability-based learning enable AI to generate human-like text and creative content at scale **Content:** ![Analyticsn Understand generative AI and large language models, including how ChatGPT works. Learn how transformers, self-attention, and probability-based learning enable AI to generate human-like text and creative content at scale Understand generative AI and large language models, including how ChatGPT works. Learn how transformers, self-attention, and probability-based learning enable AI to generate human-like text and creative content at scale](https://analyticsn.com/wp-content/uploads/2026/01/5-generative-AI-and-large-language-models-LLMs-1024x576.png "5 generative AI and large language models LLMs - Analyticsn")# Generative AI and Large Language Models: A Practical Explanation ## Introduction Generative AI has become one of the most influential developments in artificial intelligence, enabling machines to create original content rather than simply analyze or retrieve information. These systems now power tools that generate text, images, and other media in response to simple user prompts, reshaping how people interact with technology. ## What Is Generative AI? Generative AI refers to [deep learning](https://analyticsn.com/topic-3-from-artificial-intelligence-to-deep-learning/) models designed to produce new and original outputs. Depending on the model, this content may include text, images, audio, or video. Image-generation tools such as **DALL·E** and **Midjourney** demonstrate how generative AI extends beyond language, producing highly creative visuals from short textual descriptions . ## Large Language Models (LLMs) Large Language Models are a specialized class of generative AI focused specifically on **text generation**. These models analyze vast collections of written material and learn how words, phrases, and sentences typically relate to one another. Systems such as ChatGPT fall into this category, generating responses that closely resemble human writing in structure and tone . ## How Language Models Predict Text Language models do not “know” facts in the human sense. Instead, they predict the most probable next word based on context. When prompted with *“The capital of France is …”*, the model evaluates thousands of possible continuations and selects **Paris** because it has the highest probability given patterns seen in training data. This process must account for ambiguity, since words like *capital* and *France* can appear in many different contexts. As questions become more complex, the model performs deeper probabilistic reasoning. When asked *“When did Paris become the capital of France?”*, it associates the prompt with millions of similar historical statements and generates a response that best fits those patterns, introducing slight variation due to built-in randomness. ## Learning the “Shape” of Language Modern large language models are often described as learning the *shape of language*. By processing millions of documents, they become highly effective at recognizing which combinations of words naturally belong together. Early generative AI systems struggled with grammar and coherence, but increasing [model complexity and training data](https://analyticsn.com/5-data-modeling-levels-and-techniques/) have dramatically improved fluency and credibility. ## What Does GPT Mean? GPT stands for **Generative Pre-Trained Transformer**, and each component explains how these models work: - **Generative**: They produce new, original text rather than copying existing content. - **Pre-trained**: Models are trained on massive datasets before being adapted to specific tasks or domains. - **Transformer**: The deep learning architecture that enables advanced language understanding. ## Transformers and Self-Attention Transformers, introduced in 2017, were a major breakthrough in deep learning. Their defining feature is **self-attention**, which allows the model to weigh the importance of each word relative to others in a sentence. This makes it possible to understand context, manage long text sequences, and generate more coherent responses than earlier architectures. ## Scale, Cost, and Complexity Large language models are among the most complex and resource-intensive AI systems ever created. Advanced models such as GPT-4 require enormous datasets, months of computation, and substantial financial investment to train. This scale gives them unparalleled expressive power, but also makes them costly and computationally demanding. ## Conclusion Generative AI and large language models mark a shift from analytical AI to creative AI. By relying on probability, massive data exposure, and transformer-based architectures, tools like ChatGPT can generate remarkably human-like text. While they do not truly understand language, their sophistication and scale make them some of the most powerful AI systems in use today. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI content creation, AI text generation, AnalyticsN, DALL·E vs Midjourney, deep learning NLP, Generative AI, generative pre-trained transformer, GPT meaning, GPT-4 training, how ChatGPT works, large language models, modern AI models, probability-based language models, self-attention mechanism, transformer architecture --- ### [Topic 6 The Evolution of AI: From Early Neural Networks to ChatGPT](https://analyticsn.com/topic-6-the-evolution-of-ai-from-early-neural-networks-to-chatgpt/) **Published:** February 12, 2026 **Author:** AnalyticsN **Excerpt:** Discover the evolution of artificial intelligence—from early neural networks and AI winters to deep learning, transformers, and ChatGPT. Learn how 60+ years of breakthroughs led to GPT-4 and modern generative AI. **Content:** ![Analyticsn Discover the evolution of artificial intelligence—from early neural networks and AI winters to deep learning, transformers, and ChatGPT. Learn how 60+ years of breakthroughs led to GPT-4 and modern generative AI. Discover the evolution of artificial intelligence—from early neural networks and AI winters to deep learning, transformers, and ChatGPT. Learn how 60+ years of breakthroughs led to GPT-4 and modern generative AI.](https://analyticsn.com/wp-content/uploads/2026/01/6-evolution-of-AI-from-early-neural-networks-to-chatgpt-1024x576.png "6 evolution of AI from early neural networks to chatgpt - Analyticsn")# The Evolution of AI: From Early Neural Networks to ChatGPT ## Introduction AI tools such as ChatGPT may have captured global attention in late 2022, but their success is the result of **over six decades of artificial intelligence research**, combined with dramatic advances in computing power, data availability, and storage technologies. Today’s generative AI systems stand on a long foundation of breakthroughs that gradually transformed theoretical ideas into practical, world-changing technologies. ## The Birth of Artificial Intelligence (1950s–1960s) The term *artificial intelligence* was first coined in **1955**, marking the formal beginning of the field. Shortly afterward, the first neural network algorithm was implemented, containing just a single parameter. Despite its simplicity, it sparked intense interest in creating machines that could mimic human intelligence . In the mid-1960s, researchers at MIT developed **ELIZA**, the world’s first chatbot. Although not based on neural networks, ELIZA demonstrated that machines could process human input and return text responses—an early preview of conversational AI . ## The First AI Winter and Research Slowdown By the late 1960s and 1970s, AI research began to stall. Early neural networks faced architectural limitations, and many AI systems failed to deliver real-world value. As a result, funding declined and enthusiasm cooled—an era often referred to as the **AI winter** . ## Revival Through Neural Networks (1980s) AI research regained momentum in the **mid-1980s** with two major breakthroughs: - **Multi-layer perceptrons**, which allowed neural networks to become deeper and more expressive - **Backpropagation**, a learning technique that enabled models to efficiently correct their own errors These innovations dramatically improved performance and opened the door to practical applications across industries. ## AI Enters the Public Eye (1990s) In the **mid-1990s**, AI achieved a landmark victory when **IBM Deep Blue** defeated world chess champion Garry Kasparov. This event demonstrated, on a global stage, that AI systems could outperform humans in complex intellectual tasks . Around the same time, neural networks became highly effective in **handwritten document recognition**, unlocking major economic benefits and driving renewed corporate investment in AI. ## The Deep Learning Breakthrough (2000s–2010s) The **mid-2000s** marked the emergence of deep learning—neural networks with many layers capable of modeling highly complex patterns. This was a turning point that enabled modern AI capabilities. From **1955 to 2010**, neural network complexity doubled roughly every two years. In the last decade, however, that pace accelerated dramatically, with model size doubling approximately every **four months**, reflecting unprecedented technological momentum. ## The Modern AI Era: From Watson to Transformers Several milestone events defined the modern era of AI: - **IBM Watson** defeated human champions on *Jeopardy!*, showcasing advanced language understanding - **AlexNet (2012)** revolutionized image recognition, pushing AI performance close to human levels - **Siri and Alexa (2014)** brought AI assistants into everyday life - **OpenAI (2015)** was founded, accelerating research into safe and powerful AI systems - **AlphaGo (2016)** defeated world champions at Go, a far more complex game than chess These breakthroughs were powered by [deep learning](https://analyticsn.com/?p=1522) and massive computational resources. ## Transformers: The Final Piece of the Puzzle In **2017**, researchers at Google introduced **transformer architectures**, a breakthrough that transformed natural language processing. Transformers enabled models to understand context and relationships between words at scale, becoming the foundation for modern [large language models](https://analyticsn.com/topic-5-generative-ai-and-large-language-models/) . This innovation directly led to the rapid release of **GPT-1, GPT-2, GPT-3, and GPT-4** within just five years. ## Explosive Growth in Model Scale The growth in model complexity has been staggering: - **GPT-1**: ~100 million parameters - **GPT-4**: Over **1 trillion parameters** This represents a **10,000× increase in complexity**, made possible by massive datasets, advanced algorithms, and unprecedented computing power . ## From Concept to Colossal Achievement In just **65 years**, AI evolved from a newly coined term to trillion-parameter [language models capable of generating](https://analyticsn.com/?p=1538) human-like text. Each breakthrough—neural networks, backpropagation, [deep learning,](https://analyticsn.com/topic-3-from-artificial-intelligence-to-deep-learning/) and transformers—built upon the last, culminating in tools like ChatGPT that are now reshaping education, business, and research. ## Conclusion ChatGPT did not appear overnight. It is the result of decades of research, repeated setbacks, and remarkable breakthroughs in [artificial intelligence](https://analyticsn.com/topic-3-from-artificial-intelligence-to-deep-learning/). Understanding this history not only [explains how we arrived at today’s AI tools,](https://analyticsn.com/?p=1548) but also highlights why innovation in this field continues to accelerate at an extraordinary pace. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI breakthroughs, AI research timeline, AlexNet image recognition, AlphaGo vs humans, analyticsn.com, ChatGPT history, deep learning timeline, evolution of AI, GPT-4 development, growth of AI models, History of artificial intelligence, IBM Deep Blue Kasparov, modern AI evolution, neural network history, transformer models explained --- ### [Topic 7. Rise of AI Adoption: Why ChatGPT Changed Everything](https://analyticsn.com/topic-7-rise-of-ai-adoption-why-chatgpt-changed-everything/) **Published:** February 17, 2026 **Author:** AnalyticsN **Excerpt:** ChatGPT became the fastest-growing product in history, reaching 1 million users in just five days. Explore how this unprecedented AI adoption is transforming industries and accelerating the global AI revolution. **Content:** ![Analyticsn ChatGPT became the fastest-growing product in history, reaching 1 million users in just five days. Explore how this unprecedented AI adoption is transforming industries and accelerating the global AI revolution. ChatGPT became the fastest-growing product in history, reaching 1 million users in just five days. Explore how this unprecedented AI adoption is transforming industries and accelerating the global AI revolution.](https://analyticsn.com/wp-content/uploads/2026/01/7-unmatched-rise-of-AI-adoption-why-chatgpt-changed-everything-1024x576.png "7 unmatched rise of AI adoption why chatgpt changed everything - Analyticsn")# The Unmatched Rise of AI Adoption: Why ChatGPT Changed Everything ## Introduction The adoption rate of modern AI tools is unlike anything the tech industry has ever seen. When **ChatGPT** launched in late 2022, it didn’t just gain attention—it **rewrote the rules of product adoption**, becoming the fastest-growing online application in history. If it felt like ChatGPT appeared overnight, the data strongly supports that impression. ## ChatGPT vs. Other Tech Giants: A Historic Comparison To understand just how extraordinary ChatGPT’s growth has been, it helps to compare it with other major platforms: - **Netflix** took approximately **3.5 years** to reach 1 million users - **Twitter** reached the same milestone in **2 years** - **Facebook** achieved it in about **10 months** - **Instagram** accelerated growth further, hitting 1 million users in **2.5 months** - **ChatGPT**, however, reached **1 million users in just 5 days** This level of adoption is virtually unheard of for any digital product and highlights a fundamental shift in how users perceive and adopt AI-driven tools . ## Why ChatGPT’s Adoption Was So Explosive Several factors contributed to ChatGPT’s unprecedented growth: - **Ease of use**: No technical background was required to start using it - **Immediate value**: Users could generate text, ideas, code, and explanations instantly - **Broad applicability**: It appealed to students, professionals, developers, and businesses alike - **Mature underlying technology**: Decades of AI research made the product feel polished at launch These conditions created the perfect environment for viral adoption. ## The Ripple Effect Across the AI Industry ChatGPT’s success didn’t happen in isolation. Its rapid rise triggered a **cascade effect across the entire AI ecosystem**. Today, we see AI-powered applications emerging across nearly every domain, including: - Text generation and summarization - Image and video creation - Code generation and debugging - Speech synthesis and voice assistants - Music composition and audio design - 3D modeling and design tools This surge reflects a broader shift: more individuals and companies are experimenting with AI to solve **specific, real-world problems** than ever before. ## An Explosion of New AI Startups and Tools The current AI boom is characterized not only by user adoption but also by innovation. While not every new AI startup will emerge as a long-term winner, the sheer number of experiments underway signals a major transformation of traditional workflows. Many roles are being **augmented or redefined** rather than replaced, particularly in knowledge-based professions. ## Focus on the Leading AI Platforms: ChatGPT and Gemini Despite the growing number of AI tools, two platforms dominate the current landscape: - **ChatGPT**, developed by **OpenAI**, which maintains a close partnership with Microsoft - **Gemini**, developed by **Google**, formerly known as **Bard** ChatGPT gained first-mover advantage with its public release in **November 2022**, while Gemini quickly emerged as a strong competitor following its release in **early 2023**. Although the names have changed, Gemini and Bard refer to the same underlying system—a point worth noting when encountering older demonstrations or references. ## Strengths, Similarities, and Limitations Both ChatGPT and Gemini offer comparable benefits for data professionals, analysts, and knowledge workers. While each platform has unique strengths and weaknesses, they share similar limitations, particularly around accuracy, reasoning depth, and contextual understanding. Understanding these limitations is critical for using AI responsibly and effectively in professional settings. ## What This Means for the Future of Work The speed at which ChatGPT was adopted signals a broader reality: **AI is no longer a future technology—it is a present one**. As adoption continues to accelerate, entire industries are likely to be transformed or augmented by AI-powered tools. Those who learn how to work effectively with platforms like ChatGPT and Gemini today will be far better positioned as the technology continues to evolve. ## Conclusion ChatGPT’s five-day journey to one million users is more than a viral success story—it represents a turning point in technological adoption. The explosive growth of AI tools is reshaping how people work, create, and solve problems, marking the beginning of a new era in human–computer interaction. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI adoption trends, AI industry growth, AI productivity tools, AI tools for professionals, AnalyticsN, ChatGPT adoption rate, ChatGPT and Gemini comparison, ChatGPT vs Facebook growth, fastest growing app in history, future of AI work, generative AI tools, Google Gemini vs Bard, OpenAI ChatGPT history --- ### [Topic 8. Hidden Pitfalls of Artificial Intelligence: What Users Must Know](https://analyticsn.com/topic-8-hidden-pitfalls-of-artificial-intelligence-what-users-must-know/) **Published:** February 19, 2026 **Author:** AnalyticsN **Excerpt:** Artificial intelligence is powerful but imperfect. Learn about AI hallucinations, accuracy issues, lack of context, and why human verification is essential for responsible AI use. **Content:** ![Analyticsn Artificial intelligence is powerful but imperfect. Learn about AI hallucinations, accuracy issues, lack of context, and why human verification is essential for responsible AI use. Artificial intelligence is powerful but imperfect. Learn about AI hallucinations, accuracy issues, lack of context, and why human verification is essential for responsible AI use.](https://analyticsn.com/wp-content/uploads/2026/01/8-hidden-pitfalls-of-artificial-intelligence-what-users-must-know-1024x576.png "8 hidden pitfalls of artificial intelligence what users must know - Analyticsn")# The Hidden Pitfalls of Artificial Intelligence: What Users Must Know ## Introduction: AI Is Powerful—but Not Perfect Artificial intelligence has rapidly transformed research, analytics, programming, and decision-making. Modern AI tools, especially [large language models](https://analyticsn.com/?p=1538) (LLMs), can generate detailed explanations, code, and summaries with impressive fluency. However, despite their capabilities, these systems are not flawless. Understanding their limitations is essential to using them responsibly and effectively. ## 1. Hallucination of Facts: The Most Serious Risk One of the most concerning weaknesses of LLMs is their tendency to **hallucinate information with high confidence**. AI systems may generate fabricated research papers, authors, or citations that appear entirely credible. Documented cases show users receiving convincing academic-style references that do not exist, highlighting a critical reliability gap . Ultimately, users—not AI developers—remain responsible for verifying claims before relying on them in research, healthcare, or policy decisions. ## 2. Inaccurate or Inefficient Solutions AI tools can also provide **suboptimal or incorrect solutions**, particularly for analytical or technical problems. While a response may appear logically structured, it can be inefficient, overly complex, or even wrong. Because these systems do not guarantee correctness, their outputs should be reviewed as *suggestions* rather than final answers, especially in high-stakes contexts. ## 3. Limited Domain and Contextual Understanding Although LLMs perform well at explaining *what* to do and *how* to do it, they often struggle with the *why*. They lack deep domain awareness and may not fully grasp organizational goals, business logic, or contextual constraints. For example, an AI can explain how to filter sales [data but may not understand](https://analyticsn.com/data-vs-metrics-understanding-the-core-difference/) the strategic reasoning behind selecting specific products or markets. ## 4. Absence of Human Judgment and Common Sense AI systems do not possess human intuition, ethical reasoning, or common sense. They depend entirely on the inputs provided and may miss critical context that seems obvious to people. When information is incomplete or ambiguous, AI responses can deviate significantly from user intent, reinforcing the importance of [clear prompts](https://analyticsn.com/?p=1551) and human oversight . ## 5. Using AI Effectively Despite Its Limitations Recognizing these pitfalls does not mean abandoning AI tools altogether. On the contrary, awareness is the first step toward effective use. When treated as **supportive tools rather than authoritative sources**, AI systems can enhance productivity, accelerate analysis, and inspire new insights. Human verification, contextual understanding, and critical thinking remain essential to maximizing their value. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI decision-making risks, AI fact-checking, AI hallucinations, AI in research and analytics, AnalyticsN, artificial intelligence limitations, ChatGPT accuracy issues, large language model risks, limitations of LLMs, responsible use of AI --- ### [Topic 9. How to Access ChatGPT and Google Gemini: A Beginner’s Step-by-Step Guide](https://analyticsn.com/topic-9-how-to-access-chatgpt-and-google-gemini-a-beginners-step-by-step-guide/) **Published:** February 24, 2026 **Author:** AnalyticsN **Excerpt:** Learn how to access ChatGPT and Google Gemini for free. This step-by-step beginner guide explains sign-up, requirements, and how to start using today’s most popular AI tools. **Content:** ![Analyticsn Learn how to access ChatGPT and Google Gemini for free. This step-by-step beginner guide explains sign-up, requirements, and how to start using today’s most popular AI tools. Learn how to access ChatGPT and Google Gemini for free. This step-by-step beginner guide explains sign-up, requirements, and how to start using today’s most popular AI tools.](https://analyticsn.com/wp-content/uploads/2026/01/9-how-to-access-chatgpt-and-google-gemini-1024x576.png "9 how to access chatgpt and google gemini - Analyticsn")# Access ChatGPT and Google Gemini: A Beginner’s Step-by-Step Guide ## Introduction: Getting Started with AI Tools After understanding what modern AI tools can do—and where their limitations lie—the next logical step is learning **how to access them**. Fortunately, getting started with today’s most popular AI platforms is simple, fast, and free. In this guide, we’ll walk through how to access **ChatGPT** and **Google Gemini**, the two leading generative AI tools used by professionals, students, and analysts worldwide. ## How to Access ChatGPT for Free ChatGPT is developed by **OpenAI** and is available to anyone with an internet connection. The free version provides ample functionality for learning, experimentation, and everyday analytical tasks. ### **Steps to access ChatGPT:** 1. Visit **chat.openai.com** 2. Click **Create an account** on your first visit 3. Sign up using: 1. An email address, or 1. An existing **Google** or **Microsoft** account 4. Log in and begin using ChatGPT immediately OpenAI also offers a **paid premium version** that provides access to more advanced models and fewer usage limits. However, the free version is more than sufficient for most users, especially for learning and practice purposes . For official updates, feature announcements, and deeper insights, OpenAI maintains a dedicated ChatGPT blog on their website. ## How to Access Google Gemini Google Gemini is Google’s flagship AI assistant and a direct competitor to ChatGPT. It was made freely available to users in **May 2023**, expanding access to generative AI across Google’s ecosystem. ### **Steps to access Google Gemini:** 1. Visit **gemini.google.com** 2. Sign in using a **Google account** 3. If you don’t have one, you’ll be prompted to create it 4. Once logged in, you can start using Gemini instantly Gemini integrates tightly with Google services and offers similar conversational and analytical capabilities to ChatGPT. Google also provides a detailed **Gemini FAQ page** for users seeking additional technical or product information . ## ChatGPT vs. Gemini: What Beginners Should Know Both tools are: - Free to access - Easy to set up - Suitable for beginners and professionals - Comparable in core functionality While each platform has its own strengths, their benefits and limitations are broadly similar—making either a solid choice for learning and experimentation. ## Why You Should Start Practicing Now The fastest way to become effective with AI tools is hands-on experience. By setting up ChatGPT and Gemini early, users can: - Practice prompt writing - Explore real-world use cases - Learn tool limitations firsthand - Build AI-assisted workflows Early familiarity ensures you stay aligned as these platforms evolve and improve. ## Conclusion Accessing ChatGPT and Google Gemini is straightforward, free, and requires minimal setup. With just a few clicks, users can begin exploring powerful AI capabilities that are already reshaping education, analytics, and professional work. Taking the time to get started now will put you ahead as AI tools continue to advance. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI chatbots for learning, AnalyticsN, beginner AI tools, ChatGPT free version, ChatGPT login guide, ChatGPT vs Gemini, free AI tools online, Gemini AI sign up, Google Gemini access, Google Gemini AI guide, how to access ChatGPT, OpenAI ChatGPT tutorial, sign up for ChatGPT --- ### [Topic 10. Prompt Engineering Explained: How to Write Better Prompts for AI Tools](https://analyticsn.com/topic-10-prompt-engineering-explained-how-to-write-better-prompts-for-ai-tools/) **Published:** February 26, 2026 **Author:** AnalyticsN **Excerpt:** Learn what prompt engineering is and how to write better AI prompts. This beginner-friendly guide explains best practices, examples, and tips to get more accurate results from ChatGPT and Google Gemini. Improve prompts **Content:** ![Analyticsn Learn what prompt engineering is and how to write better AI prompts. This beginner-friendly guide explains best practices, examples, and tips to get more accurate results from ChatGPT and Google Gemini. Improve prompts Learn what prompt engineering is and how to write better AI prompts. This beginner-friendly guide explains best practices, examples, and tips to get more accurate results from ChatGPT and Google Gemini. Improve prompts](https://analyticsn.com/wp-content/uploads/2026/01/10-prompt-engineering-how-to-write-better-prompts-for-AI-tools-1024x576.png "10 prompt engineering how to write better prompts for AI tools - Analyticsn")# Prompt Engineering Explained: How to Write Better Prompts for AI Tools ## Introduction: Why Prompt Engineering Matters Prompt engineering has quickly become one of the most important skills for working with modern AI tools. Whether you are using ChatGPT or Google Gemini, the quality of the output you receive depends heavily on how well you phrase your input. The good news is that prompt engineering is not complex—it simply requires clarity, structure, and a bit of practice. This guide explains what prompt engineering is, how prompts work, and practical tips to help you write more effective prompts with confidence. ## What Is a Prompt? A **prompt** is the input you give to an AI system. It can be: - A question - A command - A set of instructions - A description of a task For example: *“Can you explain how the OFFSET function works in Excel?”* This prompt tells the AI exactly what information you want, and the response it generates is based entirely on how it interprets that input. ## What Is Prompt Engineering? **Prompt engineering** is the practice of crafting prompts in a way that produces the most accurate, useful, and relevant responses from AI tools. It is less about technical knowledge and more about **communication**—clearly explaining what you want, why you want it, and how detailed the response should be. All generative AI tools follow the same basic pattern: - **Prompt → AI processing → Response** The better the prompt, the better the response. ## How AI Responds to Prompts When you submit a prompt, AI tools analyze the wording, context, and intent of your request. Different prompts—even if they ask for similar information—can produce very different results. This is why small [improvements in how a prompt](https://analyticsn.com/?p=1558) is written can significantly improve output quality. ## Best Practices for Writing Effective AI Prompts ### 1. Be Clear and Specific Avoid vague instructions. The more precise your request, the better the AI can respond. Instead of asking for “help with Excel,” specify the function, task, or problem you want solved. ### 2. Provide Context When Possible Including background information helps the AI tailor its response. For example, explain whether you are a beginner or advanced user, or how you plan to use the output. ### 3. Include Examples if Relevant Examples reduce ambiguity. If you want the AI to follow a certain format or style, showing a short example can dramatically improve results. ### 4. Assign a Role to the AI You can ask the AI to respond from a specific perspective, such as: - “Act as an Excel instructor” - “Respond like a data analyst” - “Explain this to a beginner” This helps control tone, depth, and complexity. ### 5. Set the Desired Tone or Level of Detail Tell the AI whether you want a simple explanation, a technical breakdown, or step-by-step instructions. This prevents overly complex or overly basic answers. ### 6. Understand the Model’s Limitations AI tools do not reason like humans and may occasionally produce inaccurate or incomplete answers. Always review outputs critically, especially for technical or professional use. ## Improve Prompts Through Iteration One of the most [powerful advantages of AI tools](https://analyticsn.com/multiple-regression-analysis-a-powerful-tool-for-predictive-modeling/) is speed. If the first response isn’t ideal, you can: - Rephrase the prompt - Add more context - Ask follow-up questions **You do not need to write the perfect prompt on your first attempt.** Iteration is part of the process and often leads to better results than overthinking upfront. ## Pro Tip: Start Simple and Refine Rather than spending excessive time trying to craft the perfect prompt, start with a reasonable version and see what the AI returns. From there, adjust based on the response. This approach is faster, more practical, and far more effective in real-world usage. ## Conclusion Prompt engineering is a foundational skill for anyone using AI tools like ChatGPT or Google Gemini. By focusing on clarity, context, and iteration, you can dramatically [improve the quality of AI-generated responses](https://analyticsn.com/?p=1555). The key is not perfection—but thoughtful communication and continuous refinement. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI prompt best practices, AI prompt examples, AnalyticsN, beginner prompt engineering guide, ChatGPT prompt tips, effective prompts for ChatGPT, Google Gemini prompts, how to write AI prompts, prompt engineering, prompt engineering for beginners, what is prompt engineering --- ### [Topic 11. Prompt Engineering Tip #1: Be Clear and Specific to Get Better AI Results](https://analyticsn.com/topic-11-prompt-engineering-tip-1-be-clear-and-specific-to-get-better-ai-results/) **Published:** March 3, 2026 **Author:** AnalyticsN **Excerpt:** Learn how being clear and specific in prompt engineering dramatically improves AI outputs. Discover practical examples, Excel use cases, and expert tips for writing effective AI prompts. **Content:** ![Analyticsn Learn how being clear and specific in prompt engineering dramatically improves AI outputs. Discover practical examples, Excel use cases, and expert tips for writing effective AI prompts. prompt engineering tips, be clear and specific, improve AI performance, Learn how being clear and specific in prompt engineering dramatically improves AI outputs. Discover practical examples, Excel use cases, and expert tips for writing effective AI prompts.](https://analyticsn.com/wp-content/uploads/2026/01/11-prompt-engineering-tip-1-be-clear-and-specific-1024x576.png "11 prompt engineering tip 1 be clear and specific - Analyticsn")# Prompt Engineering Tip #1: Be Clear and Specific to Get Better AI Results ## **Introduction: Why Prompt Clarity Matters in AI** Prompt engineering plays a crucial role in determining the quality of responses generated by modern AI tools such as ChatGPT and Google Gemini. One of the most powerful—and often overlooked—techniques for improving AI outputs is simple: **be clear and specific**. When instructions are vague, AI responses tend to be generic. When prompts are precise, results become significantly more accurate, relevant, and usable. Get better AI results by following this Prompt Engineering tips and improve AI performance. ## **What Does “Clear and Specific” Mean in Prompt Engineering?** Being clear and specific means explicitly telling the AI: - **What task you want completed** - **Which tools or formats to use** - **Where the data is located** - **What level of detail you expect** AI systems respond best when they are given concrete instructions rather than broad or ambiguous questions. ## **Example: Improving an AI Prompt for Excel** ### **Basic Prompt (Less Effective)** “How do I calculate year-over-year growth in Excel?” This [prompt generates a reasonable response,](https://analyticsn.com/?p=1555) but it often lacks precision. The AI may explain the concept but fail to provide a ready-to-use solution. ### **Improved Prompt (Highly Effective)** “Provide an Excel formula to calculate year-over-year growth where sales data is in cells C2:C100 and the corresponding years are in B2:B100.” ### **Why This Works Better** - The AI knows **exactly what tool** to use (Excel) - The **data range is clearly defined** - The expected output is **a formula**, not a general explanation As a result, the AI delivers a precise formula along with a concise explanation, making the response immediately actionable. ## **Pro Tip: Be Extra Specific When Generating Code** When asking AI tools to generate formulas, scripts, or code: - Include **variable names** - Specify **data types** - Define **cell ranges or file structures** - Mention **desired outputs or constraints** The more structured your input, the closer the output will align with your actual task. ## **Why This Strategy Improves AI Performance** Research consistently shows that **structured and context-aware prompts lead to higher-quality AI responses** . Clear prompts reduce ambiguity, limit hallucinations, and improve efficiency—especially for technical and analytical tasks. ## **Final Takeaway** Clear and specific prompts are the foundation of effective prompt engineering. Instead of asking AI tools broad questions, treat them like highly capable assistants that need precise instructions. By clearly defining your objective, context, and constraints, you dramatically increase the usefulness and reliability of AI-generated responses. Mastering this single technique can instantly elevate your productivity with AI—no advanced technical skills required. ![Analyticsn Learn how being clear and specific in prompt engineering dramatically improves AI outputs. Discover practical examples, Excel use cases, and expert tips for writing effective AI prompts. prompt engineering tips, be clear and specific, improve AI performance, Learn how being clear and specific in prompt engineering dramatically improves AI outputs. Discover practical examples, Excel use cases, and expert tips for writing effective AI prompts.](https://analyticsn.com/wp-content/uploads/2026/01/image.png "image - Analyticsn")![Analyticsn Learn how being clear and specific in prompt engineering dramatically improves AI outputs. Discover practical examples, Excel use cases, and expert tips for writing effective AI prompts. prompt engineering tips, be clear and specific, improve AI performance, Learn how being clear and specific in prompt engineering dramatically improves AI outputs. Discover practical examples, Excel use cases, and expert tips for writing effective AI prompts.](https://analyticsn.com/wp-content/uploads/2026/01/image-1.png "image - Analyticsn")- Learn more about **[clear and specific prompt writing techniques](https://help.openai.com/en/articles/10032626-prompt-ingineering-best-practices-for-chatgpt)** from OpenAI: - Explore detailed guidelines on **[how specificity improves AI output quality](https://help.openai.com/en/articles/6654000-best-practices-for-crafting-prompts)** in this official guide by OpenAI: - Understand **[structured prompting and clarity techniques](https://learn.microsoft.com/en-in/azure/ai-services/openai/concepts/prompt-engineering)** from Microsoft: - Read about **[why precise prompts lead to better AI performance](https://techcommunity.microsoft.com/blog/educatordeveloperblog/prompt-engineering-simplified-ai-toolkits-prompt-builder/4384783)** on Microsoft: - Discover how **[clear structure and delimiters improve prompt clarity](https://www.analyticsvidhya.com/blog/2024/07/delimiters-in-prompt-engineering/)** on Analytics Vidhya: - Learn how **[prompt clarity and specificity are evaluated for accuracy](https://learn.microsoft.com/ar-sa/power-platform/release-plan/2025wave1/ai-builder/optimize-ai-driven-outcomes-prompt-accuracy-scoring)** in AI systems by Microsoft: - Explore advanced concepts like[ **prompt chaining for better context and accuracy**](https://www.ibm.com/topics/prompt-chaining) from IBM: ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIHZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGcgY2xpcC1wYXRoPSJ1cmwoI2NsaXAwXzM0M185OTUpIj4KPHBhdGggZD0iTTE0LjgxNTYgMEgxLjE4MTI1QzAuNTI4MTI1IDAgMCAwLjUxNTYyNSAwIDEuMTUzMTNWMTQuODQzOEMwIDE1LjQ4MTMgMC41MjgxMjUgMTYgMS4xODEyNSAxNkgxNC44MTU2QzE1LjQ2ODggMTYgMTYgMTUuNDgxMyAxNiAxNC44NDY5VjEuMTUzMTNDMTYgMC41MTU2MjUgMTUuNDY4OCAwIDE0LjgxNTYgMFpNNC43NDY4NyAxMy42MzQ0SDIuMzcxODhWNS45OTY4N0g0Ljc0Njg3VjEzLjYzNDRaTTMuNTU5MzggNC45NTYyNUMyLjc5Njg4IDQuOTU2MjUgMi4xODEyNSA0LjM0MDYyIDIuMTgxMjUgMy41ODEyNUMyLjE4MTI1IDIuODIxODggMi43OTY4OCAyLjIwNjI1IDMuNTU5MzggMi4yMDYyNUM0LjMxODc1IDIuMjA2MjUgNC45MzQzNyAyLjgyMTg4IDQuOTM0MzcgMy41ODEyNUM0LjkzNDM3IDQuMzM3NSA0LjMxODc1IDQuOTU2MjUgMy41NTkzOCA0Ljk1NjI1Wk0xMy42MzQ0IDEzLjYzNDRIMTEuMjYyNVY5LjkyMTg4QzExLjI2MjUgOS4wMzc1IDExLjI0NjkgNy44OTY4NyAxMC4wMjgxIDcuODk2ODdDOC43OTM3NSA3Ljg5Njg3IDguNjA2MjUgOC44NjI1IDguNjA2MjUgOS44NTkzOFYxMy42MzQ0SDYuMjM3NVY1Ljk5Njg3SDguNTEyNVY3LjA0MDYzSDguNTQzNzVDOC44NTkzNyA2LjQ0MDYzIDkuNjM0MzggNS44MDYyNSAxMC43ODc1IDUuODA2MjVDMTMuMTkwNiA1LjgwNjI1IDEzLjYzNDQgNy4zODc1IDEzLjYzNDQgOS40NDM3NVYxMy42MzQ0VjEzLjYzNDRaIiBmaWxsPSIjNDM0OTYwIi8+CjwvZz4KPGRlZnM+CjxjbGlwUGF0aCBpZD0iY2xpcDBfMzQzXzk5NSI+CjxyZWN0IHdpZHRoPSIxNiIgaGVpZ2h0PSIxNiIgZmlsbD0id2hpdGUiLz4KPC9jbGlwUGF0aD4KPC9kZWZzPgo8L3N2Zz4K) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis --- ### [Topic 12. Prompt Engineering Tip #2: How Establishing Roles Improves AI Responses](https://analyticsn.com/topic-12-prompt-engineering-tip-2-how-establishing-roles-improves-ai-responses/) **Published:** March 5, 2026 **Author:** AnalyticsN **Excerpt:** Learn how establishing roles in prompt engineering helps you get clearer, more accurate, and more actionable responses from AI tools. establishing roles improve AI responses prompt engineering tips role-based prompts **Content:** ![Analyticsn Learn how establishing roles in prompt engineering helps you get clearer, more accurate, and more actionable responses from AI tools. establishing roles improve AI responses prompt engineering tips role-based prompts Learn how establishing roles in prompt engineering helps you get clearer, more accurate, and more actionable responses from AI tools. establishing roles improve AI responses prompt engineering tips role-based prompts](https://analyticsn.com/wp-content/uploads/2026/01/12-prompt-engineering-tip-2-establish-roles-1024x576.png "12 prompt engineering tip 2 establish roles - Analyticsn")# How Establishing Roles Improves AI Responses Artificial intelligence tools like ChatGPT and Google Bard can produce impressive results—but only when they clearly understand *who* they are helping and *how* they should respond. One of the most powerful yet simple prompt engineering techniques is **establishing roles**. By defining roles for both the AI and yourself, you can dramatically improve clarity, relevance, and usefulness in AI-generated outputs. Highly relevant topics in the current article are: establishing roles, improve AI responses, prompt engineering tips, role-based prompts ## What Does “Establishing Roles” Mean in Prompt Engineering? Establishing roles means explicitly telling the AI: - **Who it should act as** (e.g., a data quality engineer, Excel expert, or Python developer) - **Who you are** (e.g., a beginner analyst, business manager, or student) This added context helps the AI tailor its language, depth, and structure to match your exact needs. ## Why Role-Based Prompts Work Better AI [language models](https://analyticsn.com/?p=1538) do not truly understand intent—they infer it from patterns and context. When roles are not defined, responses tend to be generic. Assigning roles reduces ambiguity and helps the model align its response with your expectations. Key benefits include: - More targeted explanations - Better alignment with your skill level - Clearer step-by-step guidance - Improved tone and structure ## Basic Prompt vs. Role-Based Prompt: A Simple Example **Basic Prompt:** *What’s the best way to clean data?* This produces a general, high-level response that may not suit your situation. **Improved Role-Based Prompt:** *I’m a new [data analyst](https://analyticsn.com/topic-1-data-analysts-need-ai-for-data-analytics/) at a retail company. Act as a senior data quality engineer and give me a step-by-step plan to clean retail sales data.* With roles established, the AI now understands: - Your experience level - The business context - The type of response you need As a result, the output becomes more practical, structured, and actionable. ## How to Apply Role Assignment Effectively To get the best results: 1. Clearly state your role or background 2. Assign a specific professional role to the AI 3. Ask for a format that matches your goal (steps, checklist, explanation, etc.) This technique is especially effective for: - Data analysis and cleaning - Coding and debugging - Business strategy explanations - Training and learning new tools ## Final Takeaway Establishing roles is a simple prompt engineering strategy that delivers disproportionately better results. By clarifying *who the AI is* and *who you are*, you guide the model toward responses that are more relevant, accurate, and useful. When combined with clear instructions and context, role-based prompts can significantly enhance your productivity with AI tools. ![Analyticsn Learn how establishing roles in prompt engineering helps you get clearer, more accurate, and more actionable responses from AI tools. establishing roles improve AI responses prompt engineering tips role-based prompts Learn how establishing roles in prompt engineering helps you get clearer, more accurate, and more actionable responses from AI tools. establishing roles improve AI responses prompt engineering tips role-based prompts](https://analyticsn.com/wp-content/uploads/2026/01/image-2.png "image - Analyticsn")Learn how structured prompts improve AI outputs in this **comprehensive guide to prompt engineering techniques** → [prompt engineering techniques guide](https://promptengineering.site/?utm_source=chatgpt.com) Explore academic insights on AI prompting in this **systematic review of prompt engineering in education** → [prompt engineering in higher education research](https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-025-00503-7?utm_source=chatgpt.com) Understand how assigning roles enhances responses through **persona and role-based prompting strategies** → [role-based prompting techniques explained](https://www.atalegroup.com/resource?utm_source=chatgpt.com) Get a clear definition and key methods from this **complete overview of prompt engineering concepts** → [what is prompt engineering and how it works](https://en.wikipedia.org/wiki/Prompt_engineering?utm_source=chatgpt.com) Discover why role prompting improves accuracy in this **advanced role-based prompting explanation** → [benefits of role-based prompting in AI](https://promptden.com/blog/6-advanced-prompt-engineering-examples-to-master-in-2025?utm_source=chatgpt.com) See how structured prompts increase AI performance in this **latest prompt engineering trends and techniques (2026)** → [latest prompt engineering techniques 2026](https://decodethefuture.org/en/prompt-engineering/?utm_source=chatgpt.com) Read research-backed insights on role simulation in AI from this **study on role-based prompting effectiveness** → [role prompting improves contextual AI responses](https://pmc.ncbi.nlm.nih.gov/articles/PMC12191768/?utm_source=chatgpt.com) ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI prompt best practices, AnalyticsN, ChatGPT prompt techniques, Google Bard prompts, how to write better AI prompts, improve AI responses, prompt engineering for beginners, prompt engineering tips, role-based prompting --- ### [Topic 13. How Setting the Right Tone in Prompts Improves AI Responses](https://analyticsn.com/topic-13-how-setting-the-right-tone-in-prompts-improves-ai-responses/) **Published:** March 10, 2026 **Author:** AnalyticsN **Excerpt:** Learn how setting the right tone in prompt engineering improves AI responses. Discover practical prompt engineering tips to get clearer, more accurate results from ChatGPT and Google Bard. **Content:** ![Analyticsn Learn how setting the right tone in prompt engineering improves AI responses. Discover practical prompt engineering tips to get clearer, more accurate results from ChatGPT and Google Bard. Learn how setting the right tone in prompt engineering improves AI responses. Discover practical prompt engineering tips to get clearer, more accurate results from ChatGPT and Google Bard.](https://analyticsn.com/wp-content/uploads/2026/01/13-prompt-engineering-tip-3-setting-the-right-tone-1024x576.png "13 prompt engineering tip 3 setting the right tone - Analyticsn")# Prompt Engineering Tip # 3: How Setting the Right Tone in Prompts Improves AI Responses ## Introduction Prompt engineering is not just about what you ask an AI tool—it’s also about **how you ask it**. One of the most overlooked yet powerful techniques in prompt engineering is **setting the right tone and level of explanation**. When used correctly, tone helps AI tools like ChatGPT and Google Bard generate responses that are clearer, more relevant, and better aligned with your audience’s needs. Learn how to [improve AI responses with prompt engineering](https://analyticsn.com/?p=1555) tips ## Why Tone Matters in Prompt Engineering When you give an AI a vague instruction such as *“Explain regression analysis”*, the model must guess your expectations. Are you looking for a technical explanation? A beginner-friendly overview? A business-focused summary? Without guidance, the response may miss the mark. By explicitly defining the tone, complexity, and target audience, you significantly improve the usefulness of the output. Research on [large language models](https://analyticsn.com/?p=1538) also shows that **prompt phrasing directly influences accuracy, clarity, and relevance** . ## Example: Vague Prompt vs. Tone-Optimized Prompt ### ❌ Basic Prompt Explain regression analysis. This prompt leaves too much ambiguity about depth, audience, and purpose. ### ✅ Improved Prompt with Tone Explain [regression analysis](https://analyticsn.com/multiple-regression-analysis-a-powerful-tool-for-predictive-modeling/) in two sentences to a C-level executive at a marketing agency. **Result:** The AI produces a concise, non-technical explanation that focuses on business value rather than mathematical detail—exactly what a senior executive needs. ## How to Set the Right Tone in Your Prompts To consistently get high-quality responses, include tone-setting elements such as: - **Audience type** (child, student, executive, non-technical user) - **Knowledge level** (beginner, intermediate, expert) - **Response length** (one paragraph, two sentences, bullet points) - **Communication style** (simple, professional, conversational) ### Pro Tip to improve AI response Use phrases like: *“Explain this as if you’re talking to…”* This single addition can dramatically improve relevance and readability. ## Benefits of Tone-Controlled Prompting - Produces **audience-appropriate responses** - Reduces unnecessary technical jargon - Saves time by minimizing follow-up prompts - Improves clarity for reports, presentations, and training material - Enhances reliability in professional and educational contexts ## Final Thoughts Being deliberate about tone is a simple but powerful [prompt engineering](https://analyticsn.com/topic-10-prompt-engineering-explained-how-to-write-better-prompts-for-ai-tools/) strategy. Instead of treating AI tools as one-size-fits-all solutions, guide them with clear expectations about *who* the response is for and *how* it should sound. This small adjustment can turn generic answers into highly effective, purpose-driven outputs. ![Analyticsn Learn how setting the right tone in prompt engineering improves AI responses. Discover practical prompt engineering tips to get clearer, more accurate results from ChatGPT and Google Bard. Learn how setting the right tone in prompt engineering improves AI responses. Discover practical prompt engineering tips to get clearer, more accurate results from ChatGPT and Google Bard. Prompt Engineering Tip # 3: How Setting the Right Tone in Prompts Improves AI Responses. Improve AI responses. prompt engineering tips](https://analyticsn.com/wp-content/uploads/2026/01/image-3.png "image - Analyticsn")imageFor additional resourses visit following links: ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** AI for Data Professionals, Blog, Courses, Data Analysis **Tags:** AI prompt writing techniques, AnalyticsN, beginner to advanced AI prompts, ChatGPT prompt examples, Google Bard prompt engineering, improve AI responses, large language model prompting, prompt engineering best practices, prompt engineering tips, setting the right tone in AI prompts --- ### [1. Introduction to Structural Equation Modeling](https://analyticsn.com/1-introduction-to-structural-equation-modeling/) **Published:** June 25, 2024 **Author:** AnalyticsN **Excerpt:** Structural Equation Modeling (SEM) is a powerful statistical technique that integrates path analysis, factor analysis, and regression into a unified framework for testing complex relationships among variables. It allows researchers to confirm hypothesized relationships and evaluate direct and indirect effects, while explicitly addressing measurement error to enhance accuracy. SEM is crucial for modeling complex systems involving mediation, moderation, and latent variables across various disciplines like social sciences and psychology. **Content:** ![Analyticsn Structural Equation Modeling (SEM) is a powerful statistical technique that integrates path analysis, factor analysis, and regression into a unified framework for testing complex relationships among variables. It allows researchers to confirm hypothesized relationships and evaluate direct and indirect effects, while explicitly addressing measurement error to enhance accuracy. SEM is crucial for modeling complex systems involving mediation, moderation, and latent variables across various disciplines like social sciences and psychology. Structural Equation Modeling (SEM) is a powerful statistical technique that integrates path analysis, factor analysis, and regression into a unified framework for testing complex relationships among variables. It allows researchers to confirm hypothesized relationships and evaluate direct and indirect effects, while explicitly addressing measurement error to enhance accuracy. SEM is crucial for modeling complex systems involving mediation, moderation, and latent variables across various disciplines like social sciences and psychology. introduction to structural equation modeling, purpose and importance of SEM in research - analyticsn.com](https://analyticsn.com/wp-content/uploads/2024/06/Fundamentals-of-Structure-Equation-modeling-2-1024x576.png "Fundamentals of Structure Equation modeling (2) - Analyticsn")## Definition & Purpose of Structural Equation Modeling (SEM) Structural Equation Modeling (SEM) is a powerful statistical technique used to test and estimate complex relationships among variables. Unlike simpler statistical methods that focus on individual relationships, SEM allows researchers to simultaneously examine multiple relationships within a single model. It integrates aspects of path analysis, [factor analysis,](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/) and regression into a comprehensive framework, making it particularly valuable for investigating complex theoretical models in various disciplines such as social sciences, psychology, economics, and more. Learn about [Fundamentals of Structural Equation Modeling](https://analyticsn.com/fundamentals-of-structure-equation-modeling/ "Fundamentals of Structure Equation Modeling") SEM serves multiple purposes in research. Firstly, it enables researchers to explore and confirm the relationships hypothesized between variables in a theoretical model. This is achieved through the explicit specification of both observed and latent (unobserved) variables, which helps in understanding the underlying structure of phenomena. Secondly, SEM facilitates testing intricate hypotheses by assessing the direct and indirect effects among variables. This capability is crucial for advancing theoretical understanding and refining models based on empirical data. **Key Points****Things to remember**DefinitionSEM is a powerful statistical technique for testing complex relationships among variables.IntegrationIntegrates path analysis, [factor analysis,](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/) and regression into a comprehensive frameworkMulti-RelationshipsAllows simultaneous examination of multiple relationships within a single model.ApplicationUsed across disciplines like social sciences, psychology, and economicsResearch PurposesConfirms relationships between observed and latent variablesHypotheses TestingEvaluates direct and indirect effects to refine theoretical models## Importance of Structural Equation Modeling (SEM) in Research The importance of SEM lies in its ability to handle measurement error explicitly, distinguishing it from traditional [regression analysis](https://analyticsn.com/multiple-regression-analysis-a-powerful-tool-for-predictive-modeling/). By incorporating measurement [models alongside structural equations,](https://analyticsn.com/fundamentals-of-structure-equation-modeling/) SEM improves the accuracy of estimates and enhances the robustness of findings. Moreover, SEM allows for the examination of complex models that involve mediation, moderation, and latent variables, which are difficult to analyze using simpler methods. This capability makes SEM indispensable for [researchers aiming to model complex systems and understand](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/) the interplay of variables in depth. SEM provides a sophisticated analytical approach that validates theoretical frameworks and uncovers nuanced relationships within data. Its advantages in handling complex models, incorporating measurement error, and exploring indirect effects make it a cornerstone in contemporary research across disciplines. As such, understanding SEM is essential for researchers aiming to conduct rigorous and insightful investigations into the complexities of real-world phenomena. **Key Points****Things to remember**Handling Measurement ErrorExplicitly addresses measurement error, enhancing accuracy compared to traditional regression.Improving AccuracyIncorporates measurement models with structural equations to improve estimate accuracyEnhancing RobustnessStrengthens findings by robustly handling complex relationships and data structures.Examining Complex ModelsAllows analysis of mediation, moderation, and latent variables within modelsCritical for Complex SystemsEssential for modeling intricate systems and understanding variable interplay deeplyYou can learn more on the importance of SEM in research here ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Blog, Courses, Structural Equation Modeling, Structure Equation Modeling **Tags:** accuracy enhancement, complex relationships, factor analysis, hypothesis testing, latent variables, measurement error, mediation, moderation, path analysis, regression, SEM, Structural Equation Modeling --- ### [7. Use Boolean logic: AND OR NOT](https://analyticsn.com/7-use-boolean-logic-and-or-not/) **Published:** March 3, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn In this reading, you will explore the basics of Boolean logic and learn how to use single and multiple conditions in a Boolean statement. These conditions are created with Boolean operators, including AND, OR, and NOT. These operators are similar to mathematical operators and can be used to create logical statements that filter your results. In this reading, you will explore the basics of Boolean logic and learn how to use single and multiple conditions in a Boolean statement. These conditions are created with Boolean operators, including AND, OR, and NOT. These operators are similar to mathematical operators and can be used to create logical statements that filter your results. Boolean logic, Boolean operators, AND OR NOT, Boolean statements, Boolean logic in queries, Truth table, Logical operators, Data filtering, Boolean conditions, Boolean expressions](https://analyticsn.com/wp-content/uploads/2025/02/7.-Use-Boolean-Language-1-1024x1024.png "7. Use Boolean Language - Analyticsn")In this reading, you will explore the basics of Boolean logic and learn how to use single and multiple conditions in a Boolean statement. These conditions are created with Boolean operators, including AND, OR, and NOT. These operators are similar to mathematical operators and can be used to create logical statements that filter your results. Data analysts use Boolean statements to do a wide range of [data analysis](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) tasks, such as writing queries for searches and checking for conditions when writing programming code. ![Analyticsn In this reading, you will explore the basics of Boolean logic and learn how to use single and multiple conditions in a Boolean statement. These conditions are created with Boolean operators, including AND, OR, and NOT. These operators are similar to mathematical operators and can be used to create logical statements that filter your results. In this reading, you will explore the basics of Boolean logic and learn how to use single and multiple conditions in a Boolean statement. These conditions are created with Boolean operators, including AND, OR, and NOT. These operators are similar to mathematical operators and can be used to create logical statements that filter your results.](https://analyticsn.com/wp-content/uploads/2025/02/image-5.png "image - Analyticsn")## Boolean logic example Imagine you are shopping for shoes, and are considering certain preferences: You will buy the shoes only if they are any combination of pink and grey You will buy the shoes if they are entirely pink, entirely grey, or if they are pink and grey You will buy the shoes if they are grey, but not if they have any pink These Venn diagrams illustrate your shoe preferences. AND is the center of the Venn diagram, where two conditions overlap. OR includes either condition. NOT includes only the part of the Venn diagram that doesn’t contain the exception. ![Analyticsn In this reading, you will explore the basics of Boolean logic and learn how to use single and multiple conditions in a Boolean statement. These conditions are created with Boolean operators, including AND, OR, and NOT. These operators are similar to mathematical operators and can be used to create logical statements that filter your results. In this reading, you will explore the basics of Boolean logic and learn how to use single and multiple conditions in a Boolean statement. These conditions are created with Boolean operators, including AND, OR, and NOT. These operators are similar to mathematical operators and can be used to create logical statements that filter your results. The intersection of these circles is highlighted to indicate the AND condition requires shoes to be both grey and pink. The Venn diagram that represents OR includes a circle labeled grey shoes overlapping with a circle labeled pink shoes. The entirety of both circles is highlighted to indicate the OR condition means any shoe with grey, pink, or some combination satisfies the requirement. The Venn diagram that represents NOT includes a circle labeled grey shoes overlapping with a circle labeled pink shoes. The portion of the grey shoes circle that does not intersect with the pink shoes circle is highlighted to indicate the NOT condition requires shoes to not include pink.](https://analyticsn.com/wp-content/uploads/2025/02/image-6-1024x281.png "image - Analyticsn")## Use Boolean logic in statements In queries, Boolean logic is represented in a statement written with Boolean operators. An **operator** is a symbol that names the operation or calculation to be performed. Read on to discover how you can convert your shoe preferences into Boolean statements. ### **The AND operator** Your condition is “If the color of the shoe has any combination of grey and pink, you will buy them.” The Boolean statement would break down the logic of that statement to filter your results by both colors. It would say IF (Color=”Grey”) AND (Color=”Pink”) then buy them The AND operator lets you stack both of your conditions. Below is a simple truth table that outlines the Boolean logic at work in this statement. In the **Color is Grey** column, there are two pairs of shoes that meet the color condition. And in the **Color is Pink** column, there are two pairs that meet that condition. But in the **If Grey AND Pink** column, only one pair of shoes meets both conditions. So, according to the Boolean logic of the statement, there is only one pair marked true. In other words, there is one pair of shoes that you would buy. **Color is Grey****Color is Pink****If Grey AND Pink, then Buy****Boolean Logic****Grey/True**Pink/TrueTrue/BuyTrue AND True = True**Grey/True**Black/FalseFalse/Don’t buyTrue AND False = False**Red/False**Pink/TrueFalse/Don’t buyFalse AND True = False**Red/False**Green/FalseFalse/Don’t buyFalse AND False = False ### **The OR operator** The OR operator lets you move forward if either one of your two conditions is met. Your condition is “If the shoes are grey or pink, you will buy them.” The Boolean statement would be IF (Color=”Grey”) OR (Color=”Pink”), then buy them. Notice that any shoe that meets either the **Color is Grey** or the **Color is Pink** condition is marked as true by the Boolean logic. According to the truth table below, there are three pairs of shoes that you can buy. **Color is Grey**Color is Pink**If Grey OR Pink, then Buy**Boolean LogicRed/FalseBlack/FalseFalse/Don’t buyFalse OR False = FalseBlack/FalsePink/TrueTrue/BuyFalse OR True = TrueGrey/TrueGreen/FalseTrue/BuyTrue OR False = TrueGrey/TruePink/TrueTrue/BuyTrue OR True = True### **The NOT operator** Finally, the NOT operator lets you filter by subtracting [specific conditions from the results](https://analyticsn.com/?p=1551). Your condition is “You will buy any grey shoe except for those with any traces of pink in them.” Your Boolean statement would be IF (Color=”Grey”) AND (Color=NOT “Pink”) then buy them Now, all of the grey shoes that aren’t pink are marked true by the Boolean logic for the NOT Pink condition. The pink shoes are marked false by the Boolean logic for the NOT Pink condition. Only one pair of shoes is excluded in the truth table below. **Color is Grey**Color is Pink****Boolean Logic for NOT Pink**If Grey AND (NOT Pink), then Buy****Boolean Logic**Grey/TrueRed/FalseNot False = TrueTrue/BuyTrue AND True = TrueGrey/TrueBlack/FalseNot False = TrueTrue/BuyTrue AND True = TrueGrey/TrueGreen/FalseNot False = TrueTrue/BuyTrue AND True = TrueGrey/TruePink/TrueNot True = FalseFalse/Don’t buyTrue AND False = False## The power of multiple conditions For [data analysts,](https://analyticsn.com/?p=1512) the real power of Boolean logic comes from being able to combine multiple conditions in a single statement. For example, if you wanted to filter for shoes that were grey or pink, and waterproof, you could construct a Boolean statement such as: “IF ((Color = “Grey”) OR (Color = “Pink”)) AND (Waterproof=”True”) Notice that you can use parentheses to group your conditions together. ## Key takeaways Operators are symbols that name the operation or calculation to be performed. The operators AND, OR, and NOT can be used to write Boolean statements in programming languages. Whether you are doing a search for new shoes or applying this logic to queries, Boolean logic lets you create multiple conditions to filter your results. Now that you know a little more about Boolean logic, you can start using it! ## Resources for more information Learn about who pioneered Boolean logic in this historical article: Origins of Boolean Algebra in the Logic of Classes. Find more information about using AND, OR, and NOT from these [tips for searching with Boolean operators](https://libguides.mit.edu/c.php?g=175963&p=1158594). ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques **Tags:** AND OR NOT, Boolean conditions, Boolean logic, Boolean logic in queries, Boolean operators, Boolean statements, Data filtering, Logical operators, Truth table --- ### [9. Meet Wide and Long Data: Step by Step](https://analyticsn.com/10-meet-wide-and-long-data-step-by-step/) **Published:** March 10, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn This reading outlines the steps the instructor performs in the following lecture, Meet wide and long data. In this lecture, the instructor presents wide and long data formats and discusses the types of questions each format can help you answer. Keep this guide open as you watch the lecture. It can serve as a helpful This reading outlines the steps the instructor performs in the following lecture, Meet wide and long data. In this lecture, the instructor presents wide and long data formats and discusses the types of questions each format can help you answer. Keep this guide open as you watch the lecture. It can serve as a helpful Wide and long data, Wide data format, Long data format, Population dataset, Data comparison, Annual population, Data sorting, Data attributes, Data analysis techniques, Spreadsheet data structure](https://analyticsn.com/wp-content/uploads/2025/02/9.-Meet-Wide-and-Long-Data-1024x1024.png "9. Meet Wide and Long Data - Analyticsn")This reading outlines the steps the instructor performs in the following lecture, Meet wide and long data. In this lecture, the instructor presents wide and long data formats and discusses the types of questions each format can help you answer. Keep this guide open as you watch the lecture. It can serve as a helpful reference if you need additional context or clarification while following the lecture steps. This is not a graded activity, but you can complete these steps to practice the skills demonstrated in the lecture. ### **What you****’ll need** If you would like to access the spreadsheets the instructor uses in this lecture, select the link to a dataset to create a copy. If you don’t have a Google account, download the data directly from the attachments below. Link to population datasets: [Population, Latin, and Caribbean Countries, 2010–2019, wide format](https://docs.google.com/spreadsheets/d/1aOcGeD7_8NvtEcVj2LD_79DaG2lwXrJC/template/preview#gid=2129150126) [Population, Latin, and Caribbean Countries, 2010–2019, long format](https://docs.google.com/spreadsheets/d/1NYjSNhjISWa6GUVlBifkPKStbrRzCot12BTYLo5kpqc/template/preview?resourcekey=0-Pyk5PUaVE1gYzlQtMiiHNQ#gid=932264330) OR Download data: [](https://d3c33hcgiwev3.cloudfront.net/MBhn3splQCKL8Lr2UTHRMg_3f9a14fdd3f947a2b978de8aee8804e1_Population-Latin-and-Caribbean-Countries-2010-2019-wide-format.xlsx?Expires=1711065600&Signature=Q7MJyw~pck7RhyaK86vYBj3cIausBmaV5DOHQqKgsW3Wk3aoPgzUqI10h~lxnmxwvT6G3bLPwXe5R8Xj6thKJooQmrZt0IQK5xWhfJaAWXlouZrGJLziRgzSR16Z1Z-ks3yu76DVB7s7qNyvUVrQg6mgoab~--id26RDbqMMDkk_&Key-Pair-Id=APKAJLTNE6QMUY6HBC5A) Population, Latin, and Caribbean Countries, 2010–2019, wide format XLSX File [](https://d3c33hcgiwev3.cloudfront.net/pAsDEZsvSpSwJDegUqvaGQ_5f34ed78cdcd494b821e4d8b29fdf8e1_Population-Latin-and-Caribbean-Countries-2010-2019-long-format-.xlsx?Expires=1711065600&Signature=K3~gV~Kcwesr3UoY-nEUiO047Q2MmxifpuAlIEVSIzEPGdwWL97-uVvjKlRHTJCRep7h10REiFn0RgkEo3K7DARr0OAGVhqRs0dSkkv-7EC4Dx1RJyqFCITYJhCHVq9FkDjhlrOFNxMEZVyX5dMd1U-gb9-QTxx8dJSTlS7JVao_&Key-Pair-Id=APKAJLTNE6QMUY6HBC5A) Population, Latin, and Caribbean Countries, 2010–2019, long format XLSX File ## Example 1: Examine wide data Wide data is a dataset in which every data subject has a single row with multiple columns to hold the values of various attributes of the subject. It helps compare specific attributes across different subjects. Open the [Population, Latin, and Caribbean Countries, 2010–2019, wide format](https://docs.google.com/spreadsheets/d/1ZlhSF9X-E-7LgIBXdEEgyw1Czm0dQvjJ/template/preview) spreadsheet. Each row contains all population data for one country. The population data for each year is contained in a column. Find the annual population of Argentina in row 3. In this wide format, you can quickly compare the annual population of Argentina to the annual populations of Antigua and Barbuda, Aruba, the Bahamas, or any other country. ### **Find the country with the highest population in 2010** Select column **E**, which contains each country’s 2010 population data. Right-click column header **E** and choose **Sort Z to A**. Notice that Brazil is now at the top of the list because it had the highest population in the year 2010. ### **Find the country with the lowest population in 2013** Select column **H**. Right-click column header **H** and choose **Sort A to Z**. Notice that the British Virgin Islands are now at the top because they had the lowest population of all countries in 2013. ## Example 2: Examine long data Long data is data in which each row represents one observation per subject, so each subject will be represented by multiple rows. This [data format](https://analyticsn.com/3-data-formats-in-practice-know-data-types/) is useful for comparing changes over time or making other comparisons across subjects. Open the [Population, Latin, and Caribbean Countries, 2010–2019, long format](https://docs.google.com/spreadsheets/d/19L9leX4z9WRPnlYDh_RIa_XFRwSUT7Im49fpxy32ihk/edit#gid=932264330) spreadsheet. Notice the data is no longer organized into columns by year. All of the years are now in one column. Find Argentina’s population data in rows 12-21. Each row contains one year of Argentina’s population data. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques **Tags:** Annual population, Data Analysis Techniques, Data attributes, Data comparison, Data sorting, Long data format, Population dataset, Spreadsheet data structure, Wide and long data, Wide data format --- ### [Explore the Analysis of Variance (ANOVA) in Hypothesis Testing](https://analyticsn.com/exploring-the-analysis-of-variance-anova-in-hypothesis-testing/) **Published:** January 27, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn Introduction to Statistical Testing and ANOVA Hypothesis testing is a cornerstone of quantitative research, and even as you progress in statistical expertise, certain foundational steps—such as examining descriptive statistics and data management—remain essential. In testing hypotheses, selecting the appropriate statistical tool depends on the type of variables involved. For instance, when dealing with a categorical Introduction to Statistical Testing and ANOVA Hypothesis testing is a cornerstone of quantitative research, and even as you progress in statistical expertise, certain foundational steps—such as examining descriptive statistics and data management—remain essential. In testing hypotheses, selecting the appropriate statistical tool depends on the type of variables involved. For instance, when dealing with a categorical](https://analyticsn.com/wp-content/uploads/2025/01/Analysis-of-Variance-ANOVA-in-Hypothesis-testing.png "Analysis of Variance ANOVA in Hypothesis testing - Analyticsn")## Introduction to Statistical Testing and ANOVA Hypothesis testing is a cornerstone of quantitative research, and even as you progress in statistical expertise, certain foundational steps—such as examining descriptive statistics and data management—remain essential. In testing hypotheses, selecting the appropriate statistical tool depends on the type of variables involved. For instance, when dealing with a **categorical explanatory variable** (e.g., presence or absence of depression) and a **[quantitative response variable](https://analyticsn.com/wp-content/uploads/2025/01/How-to-choose-a-statistical-test.png "How to choose a statistical test")** (e.g., number of cigarettes smoked), the **Analysis of Variance (ANOVA)** is the tool to use. ANOVA evaluates whether the means of the response variable differ significantly across the categories of the explanatory variable. ## How ANOVA Works: Comparing Means The core idea behind ANOVA lies in comparing means across groups defined by the explanatory variable. The **ANOVA F-test** determines whether the observed differences in [sample means reflect true differences in population](https://analyticsn.com/from-sample-to-population-hypothesis-testing/) means or if they could have occurred due to random variability. For example, consider a study investigating whether academic frustration levels differ across college majors. In this scenario: - **Explanatory Variable (X)**: College major (e.g., Business, English, Mathematics, Psychology). - **Response Variable (Y)**: Level of academic frustration, rated on a scale of 1 to 20. The **null hypothesis** (H0H\_0H0​) states that there is no relationship between the explanatory and response variables, implying that all group means are equal (μ1=μ2=μ3=μ4\\mu\_1 = \\mu\_2 = \\mu\_3 = \\mu\_4μ1​=μ2​=μ3​=μ4​). Conversely, the **alternative hypothesis** (HaH\_aHa​) asserts that not all group means are equal. ![Analyticsn Introduction to Statistical Testing and ANOVA Hypothesis testing is a cornerstone of quantitative research, and even as you progress in statistical expertise, certain foundational steps—such as examining descriptive statistics and data management—remain essential. In testing hypotheses, selecting the appropriate statistical tool depends on the type of variables involved. For instance, when dealing with a categorical Introduction to Statistical Testing and ANOVA Hypothesis testing is a cornerstone of quantitative research, and even as you progress in statistical expertise, certain foundational steps—such as examining descriptive statistics and data management—remain essential. In testing hypotheses, selecting the appropriate statistical tool depends on the type of variables involved. For instance, when dealing with a categorical Exploring the Analysis of Variance (ANOVA) in Hypothesis Testing](https://analyticsn.com/wp-content/uploads/2025/01/image-1.png "image - Analyticsn")## Examining Sample Data: Means and Variation Using random samples of 35 individuals from each major, the mean frustration scores were as follows: - Business: 7.3 - English: 11.8 - Mathematics: 13.2 - Psychology: 14.0 While differences in sample means are evident, the key question is whether these differences are [statistically significant](https://analyticsn.com/significance-of-statistical-inference-anova/). To answer this, ANOVA assesses two types of variation: 1. **Variation Among Sample Means**: How far apart the group means are from each other. 2. **Variation Within Groups**: How much individual data points vary within each group. The **F statistic**, which forms the basis of ANOVA, is calculated as the ratio of these two variations: F=Variation Among Sample MeansVariation Within GroupsF = \\frac{\\text{Variation Among Sample Means}}{\\text{Variation Within Groups}}F=Variation Within GroupsVariation Among Sample Means​ When the variation among sample means is large relative to the variation within groups, the F statistic will be high, providing stronger evidence against the null hypothesis. ## Boxplot Illustration of Variation Boxplots visually represent the variation within and among groups. Consider two hypothetical datasets: - **Dataset 1** (Country One): High variation within groups, leading to overlapping frustration scores among majors. This could indicate that any observed differences in means might arise by chance, supporting the null hypothesis. - **Dataset 2** (Country Two): Low variation within groups, with minimal overlap in frustration scores. Here, differences in means are more likely due to true differences in population means, supporting the alternative hypothesis. The extent of overlap in boxplots highlights the relationship between group variability and evidence against the null hypothesis. ![Analyticsn Introduction to Statistical Testing and ANOVA Hypothesis testing is a cornerstone of quantitative research, and even as you progress in statistical expertise, certain foundational steps—such as examining descriptive statistics and data management—remain essential. In testing hypotheses, selecting the appropriate statistical tool depends on the type of variables involved. For instance, when dealing with a categorical Introduction to Statistical Testing and ANOVA Hypothesis testing is a cornerstone of quantitative research, and even as you progress in statistical expertise, certain foundational steps—such as examining descriptive statistics and data management—remain essential. In testing hypotheses, selecting the appropriate statistical tool depends on the type of variables involved. For instance, when dealing with a categorical Exploring the Analysis of Variance (ANOVA) in Hypothesis Testing](https://analyticsn.com/wp-content/uploads/2025/01/image-2.png "image - Analyticsn")## Results of the ANOVA F-Test For Dataset 2 (Country Two), the ANOVA F [statistic was calculated](https://analyticsn.com/calculating-central-tendency-in-spss-statistics-stepwise-explanation/) as **46.60**, indicating that the variation among sample means was much greater than the variation within groups. The corresponding **p-value** was practically 0 (e.g., 0.0001). This tiny p-value suggests it is extremely unlikely to observe data like this if the null hypothesis were true. Specifically: - There is a 0.01% chance of incorrectly rejecting the null hypothesis (Type I Error). - There is a 99.99% confidence in accepting the alternative hypothesis. Thus, the null hypothesis is rejected, and it is concluded that there is a significant association between academic frustration levels and college major. In other words, frustration levels vary significantly across majors. ![Analyticsn Introduction to Statistical Testing and ANOVA Hypothesis testing is a cornerstone of quantitative research, and even as you progress in statistical expertise, certain foundational steps—such as examining descriptive statistics and data management—remain essential. In testing hypotheses, selecting the appropriate statistical tool depends on the type of variables involved. For instance, when dealing with a categorical Introduction to Statistical Testing and ANOVA Hypothesis testing is a cornerstone of quantitative research, and even as you progress in statistical expertise, certain foundational steps—such as examining descriptive statistics and data management—remain essential. In testing hypotheses, selecting the appropriate statistical tool depends on the type of variables involved. For instance, when dealing with a categorical Exploring the Analysis of Variance (ANOVA) in Hypothesis Testing](https://analyticsn.com/wp-content/uploads/2025/01/image-3.png "image - Analyticsn")## Conclusion and Application The ANOVA F-test provides a robust method for analyzing differences in means across groups. By comparing variation among and within groups, it determines whether observed differences are [statistically significant](https://analyticsn.com/significance-of-statistical-inference-anova/). The example demonstrates how ANOVA can reveal meaningful relationships in data, such as the association between academic frustration and college major. Moving forward, this understanding can be applied to other datasets, and [statistical tools](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/) like SAS can be used to automate these analyses efficiently. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Hypothesis Testing with Variance and Correlation **Tags:** academic frustration, alternative hypothesis, ANOVA, boxplot, college major, dataset, explanatory variable, F-test, hypothesis testing, means comparison, null hypothesis, p-value, research, response variable, sample data, SAS, Statistical significance, statistical testing, Type I Error, variation, variation among groups, variation within groups --- ### [Data-Driven vs. Data-Inspired Decision Making: Balancing for Effective Business Strategy](https://analyticsn.com/data-driven-vs-data-inspired-decision-making-balancing-for-effective-business-strategy/) **Published:** September 2, 2024 **Author:** AnalyticsN **Excerpt:** The differentiation between Data-Driven and Data-Inspired Decision Making to successfully Navigating the Balance for Effective Business Strategy **Content:** ![Analyticsn The differentiation between Data-Driven and Data-Inspired Decision Making to successfully Navigating the Balance for Effective Business Strategy The differentiation between Data-Driven and Data-Inspired Decision Making to successfully Navigating the Balance for Effective Business Strategy Data-Driven vs. Data-inspired Decision making in Business - analyticsn](https://analyticsn.com/wp-content/uploads/2024/09/Data-Driven-vs.-Data-inspired-Decision-making-in-Business.jpg "Data-Driven vs. Data-inspired Decision making in Business - Analyticsn")## Introduction A data analytics professional’s job is to provide the data necessary for decision making and inform key decisions. They also need to frame their analysis in a way that helps business leaders make the best possible decisions. In this reading, you’re going to explore the role of data in decision-making and the reasons why data analytics professionals are so important to this process. You’ll [compare data-driven and data-inspired decisions to understand](https://analyticsn.com/understanding-paired-sample-test-a-key-tool-for-comparative-analysis/) the difference between them. You’ll also check out some examples where projects failed or succeeded based on how the data was applied. Therefore, this article is entitled “Data-Driven vs. Data-Inspired Decision Making: Navigating the Balance for Effective Business Strategy” Both data-driven and data-inspired approaches are rooted in the idea that data is inherently valuable for making a decision. Well-curated data can provide information to decision-makers that improves the quality of their decisions. Remember: Data does not make decisions, but it does improve them. ![Analyticsn The differentiation between Data-Driven and Data-Inspired Decision Making to successfully Navigating the Balance for Effective Business Strategy The differentiation between Data-Driven and Data-Inspired Decision Making to successfully Navigating the Balance for Effective Business Strategy](https://analyticsn.com/wp-content/uploads/2024/09/image.png "image - Analyticsn")## **Data-driven decisions** As you’ve been learning, data-driven decision-making means using facts to guide business strategy. The phrase “data-driven decisions” means exactly that: Data is used to arrive at a decision. This approach is limited by the quantity and quality of readily-available data. If the quality and quantity of the data is sufficient, this approach can far improve decision-making. But if the data is insufficient or biased, this can create problems for decision-makers. Potential dangers of relying entirely on data-driven decision-making can include overreliance on historical data, a tendency to ignore qualitative insights, and potential biases in data collection and analysis ### **Example of a data-driven decision** making A/B testing is a simple example of [collecting data for data](https://analyticsn.com/1-data-collection-in-todays-world/)-driven decision-making. For example, a website that sells widgets has an idea for a new website layout they think will result in more people buying widgets. For two weeks, half of their website visitors are directed to the old site; the other half are directed to the new site. After those two weeks, the [analyst gathers the data](https://analyticsn.com/?p=1512) about their website visitors and the number of widgets sold for analysis. This helps the analyst understand which website layout resulted in more widget sales. If the new website performed better in producing widget sales, then the company can confidently make the decision to use the new layout! ## **Data-inspired decisions** Data-inspired decisions include the same considerations as data-driven decisions while adding another layer of complexity. They create space for people using data to consider a broader range of ideas: drawing on comparisons to related concepts, giving weight to feelings and experiences, and considering other qualities that may be more difficult to measure. Data-inspired decision-making can avoid some of the pitfalls that data-driven decisions might be prone to. ### **Example of a data-inspired decision** making A customer support center gathers customer satisfaction data (often known as a “CSAT” score). They use a simple 1–10 score along with a qualitative description in which the customer describes their experience. The customer support center manager wants to improve customer experience, so they [set a goal to improve](https://analyticsn.com/?p=1558) the CSAT score. They start by analyzing the CSAT scores and reading each of the descriptions from the customers. Additionally, they interview the people working in the customer support center. From there, the manager formulates a strategy and decides what needs to improve the most in order to raise customer satisfaction scores. While the manager certainly relies on the CSAT data in the decision-making process, input of support center representatives and other qualitative information informs the approach as well. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Academic Research, Blog, Data Analysis **Tags:** A/B testing, AnalyticsN, business analysis, Business Strategy, CSAT score, customer experience, Customer Satisfaction, Data analytics, data quality, data quantity, Data-driven Decisions, Data-Inspired Decisions, decision-making, decision-making process, qualitative insights --- ### [Need to Understand the Chi-Square Test of Independence](https://analyticsn.com/need-to-understand-the-chi-square-test-of-independence/) **Published:** February 3, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn The Chi-Square Test of Independence is a statistical tool used to evaluate relationships between two categorical variables. Unlike Analysis of Variance (ANOVA), which examines relationships involving a categorical explanatory variable and a quantitative response variable, the Chi-Square Test is specifically designed for categorical data. The test assesses whether the observed distribution of data differs significantly The Chi-Square Test of Independence is a statistical tool used to evaluate relationships between two categorical variables. Unlike Analysis of Variance (ANOVA), which examines relationships involving a categorical explanatory variable and a quantitative response variable, the Chi-Square Test is specifically designed for categorical data. The test assesses whether the observed distribution of data differs significantly Understanding Chi-Square test of independence](https://analyticsn.com/wp-content/uploads/2025/01/Understanding-Chi-Square-test-of-independence.png "Understanding Chi-Square test of independence - Analyticsn")The Chi-Square Test of Independence is a statistical tool used to evaluate relationships between two categorical variables. Unlike [Analysis of Variance (ANOVA)](https://analyticsn.com/tag/analysis-of-variance-anova/ "Analysis of Variance (ANOVA)"), which examines relationships involving a categorical explanatory variable and a quantitative response variable, the Chi-Square Test is specifically designed for categorical data. The test assesses whether the observed distribution of data differs significantly from what would be expected under the null hypothesis, which assumes no association between the variables. ## Real-World Example: Gender and Drunk Driving A notable example involves a challenge to an Oklahoma law in the 1970s that restricted the sale of 3.2% beer to males under 21 while allowing it for females of the same age. To justify the law, data from a random roadside survey of 619 drivers under 20 years of age were presented, categorizing drivers by gender and whether they had consumed alcohol within the previous two hours. The [data were summarized in a two-way table](https://analyticsn.com/8-data-table-components-a-quick-overview/) to determine if there was a relationship between gender and drunk driving. ## Setting Up the Hypotheses for Chi-Square In this case, the null hypothesis (H0H\_0H0​) asserts that there is no relationship between gender and drunk driving—indicating that the variables are independent. The alternative hypothesis (HaH\_aHa​) suggests that there is a relationship, meaning the variables are not independent. Algebraically, independence would imply that the proportion of male drunk drivers is equal to the proportion of female drunk drivers. ## Observed vs. Expected Counts The observed counts represent the actual [data collected,](https://analyticsn.com/1-data-collection-in-todays-world/) while the expected counts are calculated based on the assumption that the null hypothesis is true. The expected counts for each cell in the table are determined using the formula: ![Analyticsn The Chi-Square Test of Independence is a statistical tool used to evaluate relationships between two categorical variables. Unlike Analysis of Variance (ANOVA), which examines relationships involving a categorical explanatory variable and a quantitative response variable, the Chi-Square Test is specifically designed for categorical data. The test assesses whether the observed distribution of data differs significantly The Chi-Square Test of Independence is a statistical tool used to evaluate relationships between two categorical variables. Unlike Analysis of Variance (ANOVA), which examines relationships involving a categorical explanatory variable and a quantitative response variable, the Chi-Square Test is specifically designed for categorical data. The test assesses whether the observed distribution of data differs significantly Understanding the Chi-Square Test of Independence](https://analyticsn.com/wp-content/uploads/2025/01/image-4.png "image - Analyticsn")For example, the expected count for male drunk drivers is calculated by multiplying the total number of males and the total number of drunk drivers, then dividing by the total number of drivers. This calculation is repeated for each cell to produce a table of expected counts. ## Calculating the Chi-Square Statistic The Chi-Square statistic (X2X^2X2) quantifies the overall difference between observed and expected counts. It is computed as follows: ![Analyticsn The Chi-Square Test of Independence is a statistical tool used to evaluate relationships between two categorical variables. Unlike Analysis of Variance (ANOVA), which examines relationships involving a categorical explanatory variable and a quantitative response variable, the Chi-Square Test is specifically designed for categorical data. The test assesses whether the observed distribution of data differs significantly The Chi-Square Test of Independence is a statistical tool used to evaluate relationships between two categorical variables. Unlike Analysis of Variance (ANOVA), which examines relationships involving a categorical explanatory variable and a quantitative response variable, the Chi-Square Test is specifically designed for categorical data. The test assesses whether the observed distribution of data differs significantly Understanding the Chi-Square Test of Independence](https://analyticsn.com/wp-content/uploads/2025/01/image-5.png "image - Analyticsn")For each cell, the squared difference between observed and expected counts is divided by the expected count, and the results are summed across all cells. A larger X2X^2X2 value indicates greater discrepancy from the null hypothesis. ## Interpreting the Results For this example, the [calculated Chi-Square statistic](https://analyticsn.com/calculating-central-tendency-in-spss-statistics-stepwise-explanation/) is compared to a critical value of 3.84 (appropriate for a 2×2 table). If the statistic exceeds this threshold, the null hypothesis is rejected. However, in this case, the [test statistic](https://analyticsn.com/post-hoc-tests-for-anova-in-statistics/) is not large enough to reject the null hypothesis, indicating that the observed data do not differ significantly from the expected values. ## The Role of the p-Value The [p-value](https://analyticsn.com/wp-content/uploads/2025/01/What-is-p-value-in-Statistics.png "What is p-value in Statistics") provides a probability measure of observing a Chi-Square statistic as extreme as, or more extreme than, the one calculated, assuming the null hypothesis is true. For this test, the p-value is 0.201. Since this value is not smaller than the typical significance threshold (e.g., 0.05), there is insufficient evidence to reject the null hypothesis. This suggests that gender and drunk driving may be independent. ## Implications of the Findings The lack of a significant relationship between gender and drunk driving undermines the justification for the Oklahoma law. Consequently, the U.S. Supreme Court struck down the law as discriminatory and unjustified. This example highlights the utility of the Chi-Square Test of Independence in assessing categorical [data relationships and guiding](https://analyticsn.com/mastering-data-analysis-with-mathematical-thinking-a-guide-to-small-and-big-data-solutions/) policy decisions. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Hypothesis Testing with Variance and Correlation **Tags:** alternative hypothesis, categorical variables, Chi-Square statistic, Chi-Square Test of Independence, drunk driving, expected counts, gender, hypothesis testing, null hypothesis, observed counts, Oklahoma law, p-value, policy decisions, significance threshold, statistical analysis, two-way table, U.S. Supreme Court --- ### [Design Dashboard in Tableau: Step-wise](https://analyticsn.com/design-dashboard-in-tableau-step-wise/) **Published:** October 2, 2024 **Author:** AnalyticsN **Content:** ![Analyticsn Tableau There are many different visualization tools available i.e. to create a dashboard. One of the most powerful is Tableau, which supports a range of data sources and has advanced analytics capabilities that allow for in-depth exploration of data trends and patterns. Tableau can handle more data and larger datasets than many other tools and Tableau There are many different visualization tools available i.e. to create a dashboard. One of the most powerful is Tableau, which supports a range of data sources and has advanced analytics capabilities that allow for in-depth exploration of data trends and patterns. Tableau can handle more data and larger datasets than many other tools and Design dashboard in Tableau: step-wise](https://analyticsn.com/wp-content/uploads/2024/10/Dashboard-in-Tableau-step-wise.png "Dashboard in Tableau step-wise - Analyticsn")## **Tableau There are many different visualization tools available i.e. to create a dashboard. One of the most powerful is Tableau, which supports a range of data sources and has advanced analytics capabilities that allow for in-depth exploration of data trends and patterns. Tableau can handle more [data and larger datasets than many other tools](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/) and offers real-time data availability. Learning to use Tableau takes some time, but your efforts can be well-rewarded, as Tableau visualizations are pleasantly interactive. For a dashboard to be successful, it needs to engage users and help them learn. Tableau has put in a lot of effort to ensure that its users have a great experience and the platform is accessible to everyone. ### **Create a dashboard Here’s a process you can follow to create a dashboard, whether in Tableau or another visualization tool: **1. Identify the stakeholders who need to see the data and how they will use it Begin by asking effective questions. Check out this [dashboard requirements gathering worksheet](https://s3.amazonaws.com/looker-elearning-resources/Requirements+Gathering+Worksheet.pdf) to explore a wide range of good questions you can use to identify relevant stakeholders and their data needs. This is a great resource to help guide you through this process again and again. **2. Design the dashboard (what should be displayed) Use these [tips to help make your dashboard design clear](https://analyticsn.com/?p=1551) and easy to follow: Use a clear header to label the information. Add short text descriptions to each visualization. Show the most important information at the top. **3. Create mock-ups if desired A mockup is a simple draft of a visualization used for planning a dashboard and evaluating its progress. This is optional, but a lot of [data analysts](https://analyticsn.com/?p=1512) like to sketch out their dashboards before creating them. **4. Select the visualizations You have a lot of options here. Which [visualizations you select depends on the data](https://analyticsn.com/course-data-driven-visual-communication-in-an-educators-life/) story you are telling. If you need to show a change in values over time, line charts or bar graphs might be the best choice. If your goal is to show how each part contributes to the whole amount being reported, a pie or donut chart is probably a better choice. Two pie charts show an even distribution of 4 parts of a whole. The first pie chart is more traditional, appearing as a solid circle. The second pie chart is styled to show the same data in a doughnut shape. To learn more about choosing the right visualizations, check out Tableau’s galleries: For more samples of area charts, column charts, and other visualizations, visit the [Tableau Dashboard Showcase](https://www.tableau.com/solutions/gallery). This gallery is full of great examples that were created using real data; explore this resource on your own to get some inspiration. Explore [Tableau’s Viz of the Day](https://public.tableau.com/en-us/gallery/?tab=viz-of-the-day&type=viz-of-the-day) to check out visualizations curated by the community. These are visualizations created by Tableau users and are a great way to learn more about how other data analysts are using data visualization tools. **5. Create filters as needed Filters show certain data while hiding the rest of the data in a dashboard. This can be a big help to identify patterns while keeping the original data intact. It’s [common for data](https://analyticsn.com/six-common-problem-types-in-data-analysis/) analysts to use and share the same dashboard, but manage their part of it with a filter. To dig deeper into filters and find an example of filters in action, visit Tableau’s page on [Filter Actions](https://help.tableau.com/current/pro/desktop/en-us/actions_filter.htm). This is a useful resource to save and come back to when you start practicing using filters in Tableau on your own. Key takeaways Just like how the dashboard on an airplane shows the pilot their flight path, your dashboard does the same for your stakeholders. It helps them navigate the path of a project inside the data. If you add clear markers and highlight important points on your dashboard, users will [understand where your data](https://analyticsn.com/data-vs-metrics-understanding-the-core-difference/) story is headed. Then, you can work together to make sure the business gets where it needs to go. In our other [article](https://analyticsn.com/design-compelling-dashboards-with-tableau-for-data-analysis-and-stakeholders/ "article"), you were introduced to the data management tool known as a dashboard. In this self-reflection, you’ll examine different kinds of dashboards and consider how they are used by data analysts and their employers. As a refresher, a dashboard is a single point of access for managing a business’s information. It allows analysts to pull key information from data in a quick review by visualizing the data in a way that makes findings easy to understand. This self-reflection will help you develop insights into your own learning and prepare you to connect your knowledge of dashboards to what you know about business needs. As you answer questions—and come up with questions of your own—you will consider concepts, practices, and principles to help refine your understanding and reinforce your learning. You’ve done the hard work, so make sure to get the most out of it: This reflection will help your knowledge stick! ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Blog, Visualization **Tags:** Advanced analytics, AnalyticsN, Bar graphs, Business Intelligence, Dashboard design, Dashboard filters, Dashboard mockups, data analysis, Data exploration, Data filters, Data insights., Data patterns, Data storytelling, Data trends, Data Visualization, Donut charts, Filter actions, Interactive dashboards, Line charts, Mock-ups, Pie charts, Real-time data, Stakeholder data, Tableau, Tableau Dashboard Showcase, Visualization tools, Viz of the Day --- ### [1. Data Collection in Today’s World](https://analyticsn.com/1-data-collection-in-todays-world/) **Published:** February 10, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn In today's digital era, data collection has become an essential process across industries, driving decision-making and innovation. With the exponential growth of online interactions, businesses, governments, and researchers constantly gather data from various sources, including surveys, forms, online activity, and scientific observations. Whether through social media engagement, customer feedback, or large-scale censuses, data collection enables In today's digital era, data collection has become an essential process across industries, driving decision-making and innovation. With the exponential growth of online interactions, businesses, governments, and researchers constantly gather data from various sources, including surveys, forms, online activity, and scientific observations. Whether through social media engagement, customer feedback, or large-scale censuses, data collection enables Data collection, Data generation, Industries collect data, Online searches, Social media data, Mobile devices data, Digital photo data](https://analyticsn.com/wp-content/uploads/2025/02/1.-Data-Collection-in-Todays-World-1024x1024.png "1. Data Collection in Today's World - Analyticsn")In today’s digital era, **data collection** has become an essential process across industries, driving decision-making and innovation. With the exponential growth of online interactions, businesses, governments, and researchers constantly gather data from various sources, including surveys, forms, online activity, and scientific observations. Whether through social media engagement, customer feedback, or large-scale censuses, data collection enables organizations to understand trends, improve services, and enhance user experiences. However, ethical considerations and privacy concerns play a crucial [role in responsible](https://analyticsn.com/?p=1555) data gathering. Understanding how data is collected and used is fundamental for professionals looking to leverage insights effectively in their respective fields. Data is being generated worldwide, and we’re talking about tons of data. Every minute of every day, millions of texts and hundreds of millions of emails are sent. In addition, millions of online searches are made, lectures are viewed, and those numbers are only growing. That’s a lot of data. Let’s learn more about how it’s made and used. In this lecture, we’ll talk about how data can be generated and how industries collect data. Every piece of information is data. All that data is usually generated because of our activity in the world. These days, we spend a lot of time online. With social media and mobile devices, millions and millions of people are adding to the huge amount of data out there daily. Think about it like this. Every digital photo online is one piece of data. Every photo itself holds even more data, from the number of pixels to the colors contained in each of those pixels. But that’s not the only way data is made. We can also generate data by collecting information. This data generation and collection comes with a few more things to consider. It needs to be done with consideration of ethics so that we maintain people’s rights and privacy. We’ll learn more about that later. For now, let’s check out a real-world example. The United States Census Bureau uses forms to collect data about the country’s population. This data is used for many reasons, like school funding, hospitals, and fire departments. The Bureau also collects information about things like U.S. businesses, creating their own data in the process. The great thing about this is that others can use the data for their needs, including analysis. The annual business survey is used to determine businesses’ needs and how to provide them with resources to help them succeed. I generate [data in the analytics](https://analyticsn.com/?p=1512) I do for the healthcare industry. We run a lot of surveys to learn how patients feel about certain things related to their health care. For example, one survey asked how patients feel about telemedicine versus in-person doctor visits. The collected data helps our companies improve their patients’ care. Survey data is just one example. Data is always generated, and there are many ways to collect it. Even something as simple as an interview can help someone collect data. Imagine you’re in a job interview. To impress the hiring manager, you want to share information about yourself. The hiring manager collects and analyzes that data to help them decide whether to hire you. But it goes both ways. You could also collect your own data about the company to help you decide if the company is a good fit for you. Or you can use the data you collect to develop thoughtful questions for the interviewer. Scientists also generate data. They use a lot of observations in their work. For example, they might collect data by studying animal behavior or looking at bacteria under a microscope. Earlier, we discussed the forms the U.S. Census Bureau uses to collect data. Forms, questionnaires, and surveys are [commonly used to collect and generate data](https://analyticsn.com/six-common-problem-types-in-data-analysis/). One thing to note: data generated online doesn’t always happen directly. Have you ever wondered why some online ads seem to make really accurate suggestions or how some websites remember your preferences? This is done using cookies and small files stored on computers containing user information. Cookies can help inform advertisers about your interests and habits based on online surfing without personally identifying you. As a real-world analyst, you’ll have all kinds of data right at your fingertips and lots of it, too. Knowing how it’s been generated can help add context to the data, and knowing how to collect it can make the data analysis process more efficient. Coming up, you’ll learn how to decide what [data to collect for your analysis](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/). So, stay tuned. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques **Tags:** Advertisers and online habits, and surveys, Animal behavior study data, Annual business survey, Cookies in data collection, Data analysis process, Data collection, Data Generation, Digital photo data, Ethics in data collection, Forms, Healthcare industry data, Industries collect data, Job interview data collection, Microscope data collection, Mobile devices data, Observations in data collection, Online ads and user preferences, Online searches, Patient survey data, Privacy in data collection, questionnaires, Scientific data generation, Social media data, Telemedicine vs. in-person doctor visits, United States Census Bureau data, User information storage --- ### [6. Know the Data Type You're Working With](https://analyticsn.com/6-know-the-data-type-youre-working-with/) **Published:** February 27, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn By now you've learned a lot about data. From generated data, to collected data, to data formats, it's good to know as much as you can about the data you'll use for analysis. In this lecture, we'll talk about another way you can describe data: the data type. A data type is a specific kind of data attribute that tells what kind By now you've learned a lot about data. From generated data, to collected data, to data formats, it's good to know as much as you can about the data you'll use for analysis. In this lecture, we'll talk about another way you can describe data: the data type. A data type is a specific kind of data attribute that tells what kind Data types, Spreadsheet data types, Number data type, Text data type, String data type, Boolean data type, SQL data types, Data attributes, Error values in spreadsheets, Data formatting in spreadsheets](https://analyticsn.com/wp-content/uploads/2025/02/6.-Know-the-type-of-data-youre-working-with-1024x1024.png "6. Know the type of data you're working with - Analyticsn")By now you’ve learned a lot about data. From generated data, to collected data, to data formats, it’s good to know as much as you can about the data you’ll use for analysis. In this lecture, we’ll talk about another way you can describe data: the data type. A data type is a specific kind of data attribute that tells what kind of value the data is. In other words, a data type tells you what kind of data you’re working with. Data types can be different depending on the query language you’re using. For example, SQL allows for different [data types](https://analyticsn.com/six-common-problem-types-in-data-analysis/) depending on which database you’re using. For now, though, let’s focus on the [data types](https://analyticsn.com/six-common-problem-types-in-data-analysis/) you’ll use in spreadsheets. To help us out, we’ll use a spreadsheet already filled with data. We’ll call it “Worldwide Interests in Sweets through Google Searches.” ![Analyticsn By now you've learned a lot about data. From generated data, to collected data, to data formats, it's good to know as much as you can about the data you'll use for analysis. In this lecture, we'll talk about another way you can describe data: the data type. A data type is a specific kind of data attribute that tells what kind By now you've learned a lot about data. From generated data, to collected data, to data formats, it's good to know as much as you can about the data you'll use for analysis. In this lecture, we'll talk about another way you can describe data: the data type. A data type is a specific kind of data attribute that tells what kind Data types, Spreadsheet data types, Number data type, Text data type, String data type, Boolean data type, SQL data types, Data attributes, Error values in spreadsheets, Data formatting in spreadsheets](https://analyticsn.com/wp-content/uploads/2025/02/image-2.png "image - Analyticsn")Now, a data type in a spreadsheet can be one of three things: a number, a text or string, or a Boolean. You might find spreadsheet programs that classify them differently or include other types, but these value types cover just about any data you’ll find in spreadsheets. We’ll look at all of these in just a bit. Looking at columns B, D, and F, we find a number of [data types](https://analyticsn.com/six-common-problem-types-in-data-analysis/). Each number represents the search interest for the terms “cupcakes,” “ice cream,” and “candy” for a specific week. The closer a number is to 100, the more popular that search term was during that week. ![Analyticsn By now you've learned a lot about data. From generated data, to collected data, to data formats, it's good to know as much as you can about the data you'll use for analysis. In this lecture, we'll talk about another way you can describe data: the data type. A data type is a specific kind of data attribute that tells what kind By now you've learned a lot about data. From generated data, to collected data, to data formats, it's good to know as much as you can about the data you'll use for analysis. In this lecture, we'll talk about another way you can describe data: the data type. A data type is a specific kind of data attribute that tells what kind Data types, Spreadsheet data types, Number data type, Text data type, String data type, Boolean data type, SQL data types, Data attributes, Error values in spreadsheets, Data formatting in spreadsheets](https://analyticsn.com/wp-content/uploads/2025/02/image-3.png "image - Analyticsn")One hundred represents peak popularity. Keep in mind that in this case, 100 is a relative value, not the actual number of searches. It represents the maximum number of searches during a certain time. Think of it like a percentage on a test. All other searches are then also valued out of 100. You might notice this in other data sets as well. Gold star for 100! If you needed to, you could change the numbers into percents or other formats, like currency. These are all examples of number [data types](https://analyticsn.com/six-common-problem-types-in-data-analysis/). In column H, the data shows the most popular treat for each week, based on the search data. So as we’ll find in cell H4 for the week beginning July 28th, 2019, the most popular treat was ice cream. This is an example of a text [data type, or a string data type](https://analyticsn.com/3-data-formats-in-practice-know-data-types/), which is a sequence of characters and punctuation that contains textual information. n this example, that information would be the treats and people’s names. These can also include numbers, like phone numbers or numbers in street addresses. But these numbers wouldn’t be used for calculations. In this case they’re treated like text, not numbers. In columns C, E, and G, it seems like we’ve got some text. But the text here isn’t a text or string data type. Instead, it’s a Boolean data type. ***A Boolean data type is a data type with only two possible values: true or false.* Columns C, E, and G show Boolean data for whether the search interest for each week, is at least 50 out of 100. ere’s how it works. To get this data, we’ve created a formula that calculates whether the search interest data in columns B, D, and F is 50 or greater. In cell B4, the search interest is 14. In cell C4, we find the word false because, for this week of data, the search interest is less than 50. For each cell in columns C, E, and G, the only two possible values are true or false. We could change the formula so other words appear in these cells instead, but it’s still Boolean data. You’ll get a chance to read more about the Boolean data type soon. Let’s talk about a common [issue that people encounter in spreadsheets: mistaking data](https://analyticsn.com/from-issue-to-action-the-six-data-analysis-phases/) types with cell values. For example, in cell B57, we can create a formula to calculate data in other cells. This will give us the average of the search interests in cupcakes across all weeks in the dataset, which is about 15. ![Analyticsn By now you've learned a lot about data. From generated data, to collected data, to data formats, it's good to know as much as you can about the data you'll use for analysis. In this lecture, we'll talk about another way you can describe data: the data type. A data type is a specific kind of data attribute that tells what kind By now you've learned a lot about data. From generated data, to collected data, to data formats, it's good to know as much as you can about the data you'll use for analysis. In this lecture, we'll talk about another way you can describe data: the data type. A data type is a specific kind of data attribute that tells what kind Data types, Spreadsheet data types, Number data type, Text data type, String data type, Boolean data type, SQL data types, Data attributes, Error values in spreadsheets, Data formatting in spreadsheets](https://analyticsn.com/wp-content/uploads/2025/02/image-4.png "image - Analyticsn")The formula works because we calculated using a number data type. But if we tried it with a text or string data type, like the data in column C, we’d get an error. Error-values usually happen if a mistake is made when entering the values in the cells. The more you know your [data types](https://analyticsn.com/six-common-problem-types-in-data-analysis/) and which ones to use, the fewer errors you’ll run into. There you have it, a data type for everyone. We’re not done yet. Coming up, we’ll go deeper into the relationship between [data types,](https://analyticsn.com/six-common-problem-types-in-data-analysis/) fields, and values. See you soon. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIHZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGcgY2xpcC1wYXRoPSJ1cmwoI2NsaXAwXzM0M185OTUpIj4KPHBhdGggZD0iTTE0LjgxNTYgMEgxLjE4MTI1QzAuNTI4MTI1IDAgMCAwLjUxNTYyNSAwIDEuMTUzMTNWMTQuODQzOEMwIDE1LjQ4MTMgMC41MjgxMjUgMTYgMS4xODEyNSAxNkgxNC44MTU2QzE1LjQ2ODggMTYgMTYgMTUuNDgxMyAxNiAxNC44NDY5VjEuMTUzMTNDMTYgMC41MTU2MjUgMTUuNDY4OCAwIDE0LjgxNTYgMFpNNC43NDY4NyAxMy42MzQ0SDIuMzcxODhWNS45OTY4N0g0Ljc0Njg3VjEzLjYzNDRaTTMuNTU5MzggNC45NTYyNUMyLjc5Njg4IDQuOTU2MjUgMi4xODEyNSA0LjM0MDYyIDIuMTgxMjUgMy41ODEyNUMyLjE4MTI1IDIuODIxODggMi43OTY4OCAyLjIwNjI1IDMuNTU5MzggMi4yMDYyNUM0LjMxODc1IDIuMjA2MjUgNC45MzQzNyAyLjgyMTg4IDQuOTM0MzcgMy41ODEyNUM0LjkzNDM3IDQuMzM3NSA0LjMxODc1IDQuOTU2MjUgMy41NTkzOCA0Ljk1NjI1Wk0xMy42MzQ0IDEzLjYzNDRIMTEuMjYyNVY5LjkyMTg4QzExLjI2MjUgOS4wMzc1IDExLjI0NjkgNy44OTY4NyAxMC4wMjgxIDcuODk2ODdDOC43OTM3NSA3Ljg5Njg3IDguNjA2MjUgOC44NjI1IDguNjA2MjUgOS44NTkzOFYxMy42MzQ0SDYuMjM3NVY1Ljk5Njg3SDguNTEyNVY3LjA0MDYzSDguNTQzNzVDOC44NTkzNyA2LjQ0MDYzIDkuNjM0MzggNS44MDYyNSAxMC43ODc1IDUuODA2MjVDMTMuMTkwNiA1LjgwNjI1IDEzLjYzNDQgNy4zODc1IDEzLjYzNDQgOS40NDM3NVYxMy42MzQ0VjEzLjYzNDRaIiBmaWxsPSIjNDM0OTYwIi8+CjwvZz4KPGRlZnM+CjxjbGlwUGF0aCBpZD0iY2xpcDBfMzQzXzk5NSI+CjxyZWN0IHdpZHRoPSIxNiIgaGVpZ2h0PSIxNiIgZmlsbD0id2hpdGUiLz4KPC9jbGlwUGF0aD4KPC9kZWZzPgo8L3N2Zz4K) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques **Tags:** Boolean data type, Data attributes, Data formatting in spreadsheets, Data types, Error values in spreadsheets, Number data type, Spreadsheet data types, SQL data types, String data type, Text data type --- ### [3. Data Formats in Practice: Know Data Types](https://analyticsn.com/3-data-formats-in-practice-know-data-types/) **Published:** February 17, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn When you think about the word "format," many things might come to mind. Think of an advertisement for your favorite store. You might find it as a print ad, a billboard, or a commercial. The information is presented in the format you need. The data format is a lot like that, and choosing the right When you think about the word "format," many things might come to mind. Think of an advertisement for your favorite store. You might find it as a print ad, a billboard, or a commercial. The information is presented in the format you need. The data format is a lot like that, and choosing the right Data format, Data classification, Qualitative data, Quantitative data, Primary data, Secondary data, Internal data, External data, Continuous data, Discrete data, Nominal data, Ordinal data, Structured data, Unstructured data, Data analysis, Movie data, Movie genres, Data measurement, Data counting, Data collection, Data management, Spreadsheet data, Box office revenue, Budget data, Survey data, Questionnaire data, Census data, Customer profiles, Demographic data, HR data, Sales data, Product inventory, National average wages, Credit reports, Temperature data, Population size, Distance measurement, Movie ratings, Ranked-choice voting, Satisfaction level, Expense reports, Tax returns, Social media data, Email data](https://analyticsn.com/wp-content/uploads/2025/02/3.-Data-Formats-in-Practice-1024x1024.png "3. Data Formats in Practice - Analyticsn")When you think about the word “format,” many things might come to mind. Think of an advertisement for your favorite store. You might find it as a print ad, a billboard, or a commercial. The information is presented in the format you need. The data format is a lot like that, and choosing the right format will help you manage and use your data in the best way possible. I don’t know about you, but I sometimes get stuck between a couple of choices when choosing a movie to watch. If I’m in the mood for excitement or suspense, I might go for a thriller, but if I need a good laugh, I’ll choose a comedy. If I can’t decide between two movies, I might use some [data analysis](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) skills to compare and contrast them. Come to think of it, there really needs to be more movies about [data analysts](https://analyticsn.com/?p=1512). I’d watch that, but since we can’t watch a movie about data, at least not yet, we’ll do the next best thing: watch data about movies! When we look at the spreadsheet with movie data, we can compare different movies and movie genres. It turns out you can do the same with data and data formats. We’ll start with [quantitative and qualitative data](https://analyticsn.com/qualitative-and-quantitative-data/). If we check out column A, we’ll find the titles of the movies. This will be [qualitative data](https://analyticsn.com/qualitative-and-quantitative-data/) because it can’t be counted, measured, or easily expressed using numbers. Qualitative data is usually listed as a name, category, or description. The movie titles and cast members will be [qualitative data](https://analyticsn.com/qualitative-and-quantitative-data/) in the spreadsheet. Next up is [quantitative data](https://analyticsn.com/qualitative-and-quantitative-data/), which can be measured or counted and then expressed as a number. This is data with a certain quantity, amount, or range. Suppose the two columns show the movies’ budget and box office revenue in the spreadsheet. The data in these columns is listed in dollars, which can be counted, so we know the data is quantitative. We can go deeper into quantitative data and break it down into discrete or continuous data. Let’s look at some examples of Data formats: ## Data formats examples As with most things, it is easier for definitions to click when pairing them with examples you might encounter daily. Review each data format’s definition first, then use the examples to clarify your understanding. ## Primary versus secondary data The following [table highlights the differences between primary and secondary data](https://analyticsn.com/8-data-table-components-a-quick-overview/) and presents examples of each. **Data format classification**Definition**Examples**Primary data**Collected by a researcher from first-hand sourcesData from an interview you conducted – Data from a survey returned from 20 participants Data from questionnaires you got back from a group of workers**Secondary data**Gathered by other people or from other researchData you bought from a local data analytics firm’s customer profiles Demographic [data collected](https://analyticsn.com/1-data-collection-in-todays-world/) by a university Census data gathered by the federal government## Internal versus external data The following [table highlights the differences between internal and external data](https://analyticsn.com/8-data-table-components-a-quick-overview/) and presents examples of each. **Data format classification****Definition****Examples**Internal dataData that is stored inside a company’s own systemsWages of employees across different business units tracked by HR Sales [data by store location Product inventory levels](https://analyticsn.com/5-data-modeling-levels-and-techniques/) across distribution centersExternal dataData that is stored outside of a company or organizationNational average wages for the various positions throughout your organization Credit reports for customers of an auto dealership## Continuous versus discrete data The following [table highlights the differences between continuous and discrete data](https://analyticsn.com/8-data-table-components-a-quick-overview/) and presents examples of each. **Data format classification****Definition****Examples**Continuous dataData that is measured and can have almost any numeric valueHeight of kids in third grade classes (52.5 inches, 65.7 inches) Runtime markers in a lecture TemperatureDiscrete dataData that is counted and has a limited number of valuesNumber of people who visit a hospital on a daily basis (10, 20, 200) Maximum capacity allowed in a room Tickets sold in the current month## Qualitative versus quantitative data The following [table highlights the differences between qualitative and quantitative data](https://analyticsn.com/8-data-table-components-a-quick-overview/) and presents examples of each. **Data format classification****Definition****Examples**QualitativeA subjective and explanatory measure of a quality or characteristicFavorite exercise activity Brand with best customer service Fashion preferences of young adultsQuantitativeA specific and objective measure, such as a number, quantity, or rangePercentage of board certified doctors who are women Population size of elephants in Africa Distance from Earth to Mars at a particular time## Nominal versus ordinal data The following [table highlights the differences between nominal and ordinal data](https://analyticsn.com/8-data-table-components-a-quick-overview/) and presents examples of each. **Data format classification****Definition****Examples**NominalA [type of qualitative data](https://analyticsn.com/six-common-problem-types-in-data-analysis/) that is categorized without a set orderFirst-time customer, returning customer, regular customer New job applicant, existing applicant, internal applicant New listing, reduced price listing, foreclosureOrdinalA type of [qualitative data](https://analyticsn.com/qualitative-and-quantitative-data/) with a set order or scaleMovie ratings (number of stars: 1 star, 2 stars, 3 stars) Ranked-choice voting selections (1st, 2nd, 3rd) Satisfaction level measured in a survey (satisfied, neutral, dissatisfied)## Structured versus unstructured data The following table highlights the differences between [structured and unstructured data](https://analyticsn.com/4-exploration-of-structured-vs-unstructured-data/) and presents examples of each. **Data format classification****Definition****Examples**Structured dataData organized in a certain format, like rows and columnsExpense reports Tax returns Store InventoryUnstructured dataData that cannot be stored as columns and rows in a relational database. Social media posts Emails Lectures ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques **Tags:** Box office revenue, Budget data, Census data, Continuous data, Credit reports, Customer profiles, data analysis, Data classification, Data collection, Data counting, Data format, Data Management, Data measurement, Demographic data, Discrete data, Distance measurement, Expense reports, External data, HR data, Internal data, Movie data, Movie genres, Movie ratings, National average wages, Nominal data, Ordinal data, Population size, Primary data, Product inventory, qualitative data, quantitative data, Questionnaire data, Ranked-choice voting, Sales data, Satisfaction level, Secondary data, Social media data, Spreadsheet data, Structured data, Survey data, Tax returns, Temperature data, Unstructured data --- ### [Design Compelling Dashboards with Tableau: For Data Analysis and Stakeholders](https://analyticsn.com/design-compelling-dashboards-with-tableau-for-data-analysis-and-stakeholders/) **Published:** October 2, 2024 **Author:** AnalyticsN **Content:** ![Analyticsn Dashboards are powerful visual tools that help you tell your data story. A dashboard is a tool that monitors live, incoming data. It organizes information from multiple datasets into one central location, offering huge time savings. Data analysts use dashboards to track, analyze, and visualize data in order to answer questions and solve problems. For Dashboards are powerful visual tools that help you tell your data story. A dashboard is a tool that monitors live, incoming data. It organizes information from multiple datasets into one central location, offering huge time savings. Data analysts use dashboards to track, analyze, and visualize data in order to answer questions and solve problems. For Design Compelling Dashboards with Tableau: For data analysis and stakeholders](https://analyticsn.com/wp-content/uploads/2024/10/Design-Compelling-Dashboards.jpg "Design Compelling Dashboards - Analyticsn")Dashboards are powerful visual tools that help you tell your data story. A dashboard is a tool that monitors live, incoming data. It organizes information from multiple datasets into one central location, offering huge time savings. Data analysts use dashboards to track, analyze, and [visualize data](https://analyticsn.com/course-data-driven-visual-communication-in-an-educators-life/) in order to answer questions and solve problems. For a basic idea of what dashboards look like, refer to this article: “[Real-world examples of business intelligence dashboards.](https://www.tableau.com/learn/articles/business-intelligence-dashboards-examples)” **The beauty of dashboards The following table summarizes the benefits of using a dashboard for both [data analysts](https://analyticsn.com/?p=1512) and their stakeholders. **Benefits**For data analysts**For stakeholdersCentralizationShare a single source of data with all stakeholdersWork with a comprehensive view of data, initiatives, objectives, projects, processes, and moreVisualizationShow and update live, incoming data in real time\*Spot changing trends and patterns more quicklyInsightfulnessPull relevant information from different datasetsUnderstand the story behind the numbers to keep track of goals and make data-driven decisionsCustomizationCreate custom views dedicated to a specific person, project, or presentation of the dataDrill down to more specific areas of specialized interest or concernIt’s important to remember that changed data is pulled into dashboards automatically only if the data structure is the same. If the [data structure](https://analyticsn.com/4-exploration-of-structured-vs-unstructured-data/) changes, you have to update the dashboard design before the data can update live. **Tableau There are many different visualization tools available. One of the most powerful is Tableau, which supports a range of data sources and has advanced analytics capabilities that allow for in-depth exploration of data trends and patterns. Tableau can handle more [data and larger datasets than many other tools](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/) and offers real-time data availability. It does take some time to learn to use Tableau, but your efforts can be well-rewarded, as Tableau visualizations are pleasantly interactive. For a dashboard to be successful, it needs to engage users and help them learn. Tableau has put in a lot of effort to ensure that its users have a great experience and the platform is accessible to everyone. Learn to use Tableau [here](https://analyticsn.com/design-dashboard-in-tableau-step-wise/ "here") ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Blog, Visualization **Tags:** AnalyticsN, Business dashboards, Business Intelligence, Custom dashboards, Dashboard examples, Dashboards, data analysis, Data analytics, Data centralization, Data insights, Data Management, Data storytelling, Data trends, Data Visualization, Data-driven Decisions, Interactive dashboards, Real-time data, Stakeholder reporting, Tableau, Tableau dashboards, Visualization tools --- ### [How Data Empowers Decision in Data Analytics](https://analyticsn.com/how-data-empowers-decision-in-data-analytics/) **Published:** August 30, 2024 **Author:** AnalyticsN **Excerpt:** Data analysis is vital for informed decision-making, optimizing processes, and solving problems. Companies like Google use it to enhance efficiency, such as cutting data center energy use by 40%. Despite its potential, challenges in data access and interpretation underscore the importance of skilled analysts. **Content:** ![Analyticsn Data analysis is vital for informed decision-making, optimizing processes, and solving problems. Companies like Google use it to enhance efficiency, such as cutting data center energy use by 40%. Despite its potential, challenges in data access and interpretation underscore the importance of skilled analysts. Data analysis is vital for informed decision-making, optimizing processes, and solving problems. Companies like Google use it to enhance efficiency, such as cutting data center energy use by 40%. Despite its potential, challenges in data access and interpretation underscore the importance of skilled analysts. Informed Decision-Making, Data Analysis, Optimization, Efficiency, Problem-Solving, Knowledge Transformation, Data-Driven Decisions, Data-Inspired Decisions, Google, Energy Consumption, Hiring Processes, Data Generation, Data Interpretation, Data Analytics Challenges, Data Analysts](https://analyticsn.com/wp-content/uploads/2024/08/How-Data-Empowers-Decisions-Analyticsn.com_.jpg "How Data Empowers Decisions - Analyticsn.com - Analyticsn")**Informed Decision-Making**: Data analysis reveals important patterns and insights, enabling more informed and accurate decision-making in Data Analytics **Optimization and Efficiency**: Data helps organizations optimize processes, such as Google reducing energy consumption in data centers by over 40% through data analysis. **Enhanced Problem-Solving**: With the vast amount of data available, organizations can tackle larger problems and develop more powerful solutions. **Turning Data into Knowledge**: By [interpreting and contextualizing data](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/), it can be transformed into actionable knowledge, empowering more effective decisions. **Definition and Importance of Data**: - Data is a collection of facts, and data analysis reveals important patterns and insights. - Data analysis helps make more informed decisions. **Data in Decision-Making**: - Data-driven decisions: Use data directly to make decisions. - Data-inspired decisions: Explore different data sources to find commonalities. **Real-Life Example**: - Searching for “restaurants near me” and sorting by rating is an example of using data for decision-making. **Google’s Use of Data**: - Google uses data to reduce energy consumption in data centers by over 40%. - Google’s People Operations team uses data to improve hiring processes, ensuring smooth onboarding and efficient candidate selection. **Data Generation**: - 90% of the world’s data has been created in the last few years, showing the vast potential of data. **Turning Data into Knowledge**: - Data alone provides little value until it’s interpreted and given context, turning it into useful information and eventually knowledge. **Limitations of Data Analytics**: - Challenges include not having access to all necessary data or data being measured differently, which can complicate analysis. **Role of Data Analysts**: - Data analysts are crucial in providing businesses with information needed to solve problems and make decisions. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Academic Research, Blog, Data Analysis **Tags:** AnalyticsN, data analysis, Data Analysts, Data Analytics Challenges, Data Generation, Data Interpretation, Data-driven Decisions, Data-Inspired Decisions, Efficiency, Energy Consumption, Google, Hiring Processes, Informed Decision-Making, Knowledge Transformation, Optimization, Problem-Solving --- ### [From issue to action: The six data analysis phases](https://analyticsn.com/from-issue-to-action-the-six-data-analysis-phases/) **Published:** August 8, 2024 **Author:** AnalyticsN **Excerpt:** Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes. **Content:** ![Analyticsn Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes. Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes. Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes.](https://analyticsn.com/wp-content/uploads/2024/08/Lec1.-Six-Data-analysis-phases.png "Lec1. Six Data analysis phases - Analyticsn")There are six data analysis phases that will help you make seamless decisions: ask, prepare, process, analyze, share, and act. Keep in mind, these are different from the data life cycle, which describes the changes data goes through over its lifetime. Going through the steps will help you solve all kinds of business problems that you might face on the job. ![Analyticsn Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes. Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes.](https://analyticsn.com/wp-content/uploads/2024/08/image-1024x243.png "image - Analyticsn")## Step 1: Ask It’s impossible to solve a problem if you don’t know what it is. These are some things to consider: - Define the problem you’re trying to solve - Make sure you fully understand the stakeholder’s expectations - Focus on the actual problem and avoid any distractions - Collaborate with stakeholders and keep an open line of communication - Take a step back and see the whole situation in context - Questions to ask yourself in this step: - What are my stakeholders saying their problems are? - Now that I’ve identified the issues, how can I help the stakeholders resolve their questions? ![Analyticsn Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes. Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes.](https://analyticsn.com/wp-content/uploads/2024/08/image-1-1024x243.png "image-1 - Analyticsn")## Step 2: Prepare You will decide what [data you need to collect](https://analyticsn.com/1-data-collection-in-todays-world/) in order to answer your questions and how to organize it so that it is useful. You might use your business task to decide: - What metrics to measure - Locate data in your database - Create security measures to protect that data - Questions to ask yourself in this step: - What do I need to figure out how to solve this problem? - What research do I need to do? ![Analyticsn Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes. Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes.](https://analyticsn.com/wp-content/uploads/2024/08/image-2-1024x243.png "image-2 - Analyticsn")## Step 3: Process Clean data is the best data and you will need to clean up your data to get rid of any possible errors, inaccuracies, or inconsistencies. This might mean: - Using spreadsheet functions to find incorrectly entered data - Using SQL functions to check for extra spaces - Removing repeated entries - Checking as much as possible for bias in the data - Questions to ask yourself in this step: - What data errors or inaccuracies might get in my way of getting the best possible answer to the problem I am trying to solve? - How can I clean my data so the information I have is more consistent? ![Analyticsn Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes. Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes.](https://analyticsn.com/wp-content/uploads/2024/08/image-3-1024x243.png "image-3 - Analyticsn")## Step 4: Analyze You will want to think [analytically about your data](https://analyticsn.com/?p=1512). At this stage, you might sort and [format your data](https://analyticsn.com/3-data-formats-in-practice-know-data-types/) to make it easier to: - Perform calculations - Combine data from multiple sources - Create tables with your results - Questions to ask yourself in this step: - What story is my data telling me? - How will my data help me solve this problem? - Who needs my company’s product or service? What type of person is most likely to use it? ![Analyticsn Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes. Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes.](https://analyticsn.com/wp-content/uploads/2024/08/image-4-1024x246.png "image-4 - Analyticsn")## Step 5: Share Everyone shares their results differently so be sure to summarize your results with clear and enticing [visuals of your analysis using data](https://analyticsn.com/course-data-driven-visual-communication-in-an-educators-life/) via tools like graphs or dashboards. This is your chance to show the stakeholders you have solved their problem and how you got there. Sharing will certainly help your team: - Make better decisions - Make more informed decisions - Lead to stronger outcomes - Successfully communicate your findings - Questions to ask yourself in this step: - How can I make what I present to the stakeholders engaging and easy to understand? - What would help me understand this if I were the listener? ![Analyticsn Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes. Learn about the six essential data analysis phases: ask, prepare, process, analyze, share, and act. Discover how these steps help solve business problems through structured thinking, from defining issues and cleaning data to sharing results and taking action based on analysis. Perfect for new data analysts to understand and implement effective decision-making processes.](https://analyticsn.com/wp-content/uploads/2024/08/image-5-1024x243.png "image-5 - Analyticsn")## Step 6: Act Now it’s time to act on your data. You will take everything you have learned from your data analysis and put it to use. This could mean providing your stakeholders with recommendations based on your findings so they can make data-driven decisions. Questions to ask yourself in this step: - How can I use the feedback I received during the share phase (step 5) to actually meet the stakeholder’s needs and expectations? - These six steps can help you to break the data analysis process into smaller, manageable parts, which is called structured thinking. This process involves four basic activities: - Recognizing the current problem or situation - Organizing available information - Revealing gaps and opportunities - Identifying your options When you are starting out in your career as a data analyst, it is normal to feel pulled in a few different directions with your role and expectations. Following processes like the ones outlined here and using structured thinking skills can help get you back on track, fill in any gaps and let you know exactly what you need. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Blog, Data Analysis, Data Analysis **Tags:** Analytical Thinking, Business Metrics, Business Problems, data analysis, Data Analysis Techniques, Data Cleaning, Data Consistency, Data Lifecycle, Data Management, Data Organization, Data Preparation, Data Recommendations, Data Security, Data Sharing, Data Visualization, Data-driven Decisions, Decision Making, Problem Solving, SQL Functions, Stakeholder Communication, step-by-step guide, Structured Thinking --- ### [4. Exploration of Structured vs. Unstructured Data](https://analyticsn.com/4-exploration-of-structured-vs-unstructured-data/) **Published:** February 20, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn Data is everywhere, and it can be stored in many ways. Two general categories of data are: Structured data: Organized in a certain format, such as rows and columns. Unstructured data: Not organized in any easy-to-identify way. For example, you create structured data when you rate your favorite restaurant online. But when you use Google Data is everywhere, and it can be stored in many ways. Two general categories of data are: Structured data: Organized in a certain format, such as rows and columns. Unstructured data: Not organized in any easy-to-identify way. For example, you create structured data when you rate your favorite restaurant online. But when you use Google](https://analyticsn.com/wp-content/uploads/2025/02/4.-Exploration-of-Structured-vs-Unstructured-Data-1024x1024.png "4. Exploration of Structured vs Unstructured Data - Analyticsn")Data is everywhere, and it can be stored in many ways. Two general categories of data are: **Structured data:** Organized in a certain format, such as rows and columns. **Unstructured data:** Not organized in any easy-to-identify way. For example, you create structured data when you rate your favorite restaurant online. But when you use Google Earth to check out a satellite image of a restaurant location, you’re using unstructured data. Here’s a refresher on the characteristics of structured and unstructured data: ![Analyticsn Data is everywhere, and it can be stored in many ways. Two general categories of data are: Structured data: Organized in a certain format, such as rows and columns. Unstructured data: Not organized in any easy-to-identify way. For example, you create structured data when you rate your favorite restaurant online. But when you use Google Data is everywhere, and it can be stored in many ways. Two general categories of data are: Structured data: Organized in a certain format, such as rows and columns. Unstructured data: Not organized in any easy-to-identify way. For example, you create structured data when you rate your favorite restaurant online. But when you use Google - Defined data types - Most often quantitative data - Easy to organize - Easy to search - Easy to analyze - Stored in relational databases - Contained in rows and columns - Examples: Excel, Google Sheets, SQL, customer data, phone records, transaction history Unstructured data: - Varied data types - Most often qualitative data - Difficult to search - Provides more freedom for analysis - Stored in data lakes and NoSQL databases - Can't be put in rows and columns - Examples: Text messages, social media comments, phone call transcriptions, various log files, images, audio, lecture](https://analyticsn.com/wp-content/uploads/2025/02/image-1.png "image - Analyticsn")## Structured data As we described earlier, **structured [data](https://analyticsn.com/3-data-formats-in-practice-know-data-types/)** is organized in a certain format. This makes it easier to store and query for business needs. If the data is exported, the structure goes along with the data. For example, if you need to analyze data about the unstructured data in emails, photos, and social media sites, it’ll most likely be structured for analysis before you even get to it. Because of that, I want to explore structured data a bit more. As a quick refresher, structured [data is data organized in a format](https://analyticsn.com/3-data-formats-in-practice-know-data-types/) like rows and columns. But there’s definitely more to it than that. Structured [data works nicely within a data model](https://analyticsn.com/our-services/amos-experts-for-data-modeling/), which organizes data elements and how they relate to one another. What are data elements? They’re pieces of information, such as people’s names, account numbers, and addresses. Data models help to keep data consistent and provide a map of how data is organized. This makes it easier for analysts and other stakeholders to make sense of their [data and use it for business](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) purposes. Structured data is also useful for databases. In addition to working well within [data models,](https://analyticsn.com/our-services/amos-experts-for-data-modeling/) it is also useful for storing and analyzing data. This makes it easy for analysts to enter, query, and analyze the data whenever necessary. This also makes data visualization easy because structured data can be applied directly to charts, graphs, heat maps, dashboards, and most other visual representations of data. Spreadsheets and [databases that store data sets are widely](https://analyticsn.com/10-meet-wide-and-long-data-step-by-step/) used structured data sources. After exploring other [data structures, you’ll check out more data types](https://analyticsn.com/six-common-problem-types-in-data-analysis/) using a spreadsheet. ## Unstructured data **Unstructured data** can’t be organized in any easily identifiable manner. And there is much more unstructured than structured [data in the world](https://analyticsn.com/1-data-collection-in-todays-world/). Lecture and audio files, text files, social media content, satellite imagery, presentations, PDF files, open-ended survey responses, and websites qualify as unstructured data types. Most of the data being generated right now is actually unstructured. Audio files, lecture files, emails, photos, and social media are all examples of unstructured data. These can be harder to analyze in their unstructured format. ## The fairness issue The lack of structure makes unstructured data difficult to search, manage, and analyze. However, recent advancements in [artificial intelligence and machine learning](https://analyticsn.com/topic-3-from-artificial-intelligence-to-deep-learning/) algorithms are beginning to change that. Data scientists’ new challenge is ensuring these tools are inclusive and unbiased. Otherwise, certain dataset elements will be more heavily weighted and/or represented than others. As you’re learning, an unfair dataset does not accurately represent the population, causing skewed outcomes, low accuracy levels, and unreliable analysis. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques --- ### [5. Data Modeling Levels and Techniques](https://analyticsn.com/5-data-modeling-levels-and-techniques/) **Published:** February 24, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn This reading introduces you to data modeling and different types of data models. Data models help keep data consistent and enable people to map out how data is organized. A basic understanding makes it easier for analysts and other stakeholders to understand and use their data correctly. Important note: You won't be asked to design This reading introduces you to data modeling and different types of data models. Data models help keep data consistent and enable people to map out how data is organized. A basic understanding makes it easier for analysts and other stakeholders to understand and use their data correctly. Important note: You won't be asked to design Data modeling, Data models, Conceptual data modeling, Logical data modeling, Physical data modeling, Entity Relationship Diagram (ERD), Unified Modeling Language (UML), Database design, Data structure, Data analysis](https://analyticsn.com/wp-content/uploads/2025/02/5.-Data-Modelling-Levels-and-Techniques-1024x1024.png "5. Data Modelling Levels and Techniques - Analyticsn")This reading introduces you to data modeling and different types of data models. Data models help keep data consistent and enable people to map out how data is organized. A basic understanding makes it easier for analysts and other stakeholders to understand and use their data correctly. **Important note:** You won’t be asked to design a [data model as a junior data analyst](https://analyticsn.com/?p=1512). However, you might come across existing [data models](https://analyticsn.com/our-services/amos-experts-for-data-modeling/) your organization already has in place. ## What is data modeling? **Data modeling** is the process of creating diagrams that visually represent how [data is organized and structured](https://analyticsn.com/4-exploration-of-structured-vs-unstructured-data/). These [visual representations are called **data**](https://analyticsn.com/course-data-driven-visual-communication-in-an-educators-life/) models. You can think of data modeling as a blueprint of a house. At any point, there might be electricians, carpenters, and plumbers using that blueprint. Each one of these builders has a different relationship to the blueprint, but they all need it to understand the overall structure of the house. Data models are similar; different users might have different [data needs, but the data model](https://analyticsn.com/our-services/amos-experts-for-data-modeling/) gives them an understanding of the structure as a whole. ## Levels of data modeling Each level of data modeling has a different level of detail. **Conceptual data modeling** gives a high-level view of the data structure, such as how data interacts across an organization. It doesn’t contain technical details and may be used to define the business requirements for a new database. **Logical data modeling** focuses on the technical details of a database, such as relationships, attributes, and entities. For example, a logical [data model](https://analyticsn.com/our-services/amos-experts-for-data-modeling/) defines how individual records are uniquely identified in a database. However, it doesn’t spell out the actual names of database tables; that’s the job of a physical data model. **Physical data modeling** depicts how a database operates. It defines all entities and attributes used, including table names, column names, and data types. More information can be found in this [comparison of data models.](https://www.1keydata.com/datawarehousing/data-modeling-levels.html) ## Data-modeling techniques There are a lot of approaches when it comes to developing [data models,](https://analyticsn.com/our-services/amos-experts-for-data-modeling/) but two common methods are the **Entity Relationship Diagram (ERD)** and the **Unified Modeling Language (UML)** diagram. ERDs are a visual way to [understand the relationship between entities in the data](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/) model. UML diagrams are very detailed diagrams that describe the structure of a system by showing the system’s entities, attributes, operations, and relationships. As a junior data analyst, you must [understand that there are different data](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/) modeling techniques, but in practice, you will probably use your organization’s existing technique. You can read more about ERD, UML, and data dictionaries in this [data modeling techniques article](https://dataedo.com/blog/basic-data-modeling-techniques). ## Data analysis and data modeling Data modeling can help you [explore the high-level details of your data](https://analyticsn.com/2-select-the-right-data-for-exploration/) and how it is related across the organization’s information systems. Data modeling sometimes requires [data analysis](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) to understand how the data is put together; that way, you know how to map the data. And finally, [data models](https://analyticsn.com/our-services/amos-experts-for-data-modeling/) make it easier for everyone in your organization to understand and collaborate with you on your data. This is important for you and everyone on your team! ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques **Tags:** Conceptual data modeling, data analysis, Data modeling, Data models, Data structure, Database design, Entity Relationship Diagram (ERD), Logical data modeling, Physical data modeling, Unified Modeling Language (UML) --- ### [Qualitative and Quantitative Data](https://analyticsn.com/qualitative-and-quantitative-data/) **Published:** September 3, 2024 **Author:** AnalyticsN **Excerpt:** Qualitative and Quantitative data are the two major types, which are being utilized in every field of research and analysis. All have applications in business analysis, complementary use, decision making, and enhanced insights. **Content:** ![Analyticsn Qualitative and Quantitative data are the two major types, which are being utilized in every field of research and analysis. All have applications in business analysis, complementary use, decision making, and enhanced insights. Qualitative and Quantitative data are the two major types, which are being utilized in every field of research and analysis. All have applications in business analysis, complementary use, decision making, and enhanced insights. QUALITATIVE & QUANTITATIVE Data in Business - Analyticsn](https://analyticsn.com/wp-content/uploads/2024/09/QUALITATIVE-QUANTITATIVE-Data-in-Business.jpg "QUALITATIVE & QUANTITATIVE Data in Business - Analyticsn")## Introduction Qualitative and Quantitative data are the two major types, which are being utilized in every field of research and analysis. All have application in business analysis, complementary use, decision making, and enhanced insights. 1. **Two Types of Data**: Qualitative data provides the “why” through tools like focus groups, interviews, and social media analysis, while quantitative data provides the “what” through structured interviews, surveys, and polls. 2. **Application in Business Analysis**: Quantitative data is essential for tracking metrics such as movie attendance, profitability, and audience preferences over time, while qualitative data offers deeper insights into customer behavior and preferences. 3. **Complementary Use**: Combining both qualitative and quantitative data enables a comprehensive understanding of trends, allowing businesses to make informed decisions, such as adjusting showtimes or revamping concession menus. 4. **Decision Making Example**: For a movie theater, quantitative data can reveal attendance patterns and profitability margins, while qualitative data can explain customer preferences, such as preferred showtimes and sensitivity to ticket prices. 5. **Enhanced Insights**: Using both data types, businesses can uncover valuable insights—like why customers prefer certain times or amenities—that would be missed if only quantitative data were analyzed. ## Meaning of qualitative and quantitative data As you have learned, there are two types of data: qualitative and quantitative. As for Example, two silhouettes looking at each other and qualitative and quantitative data tools in two columns. Qualitative data tools: focus groups, social media text analysis, and in-person interviews Quantitative [data tools: structured](https://analyticsn.com/4-exploration-of-structured-vs-unstructured-data/) interviews, surveys, and polls Now, take a closer look at the [data types](https://analyticsn.com/six-common-problem-types-in-data-analysis/) and data collection tools. In this scenario, you are a data analyst for a chain of movie theaters. Your manager wants you to track trends in: **Movie attendance over time** **Profitability of the concession stand** **Evening audience preferences** Assume quantitative data already exists to monitor all three trends. Movie attendance over time Image of a progress measuring meter Starting with the historical data the theater has through its loyalty and rewards program, your first step is to investigate what insights you can gain from that data. You look at attendance over the last 3 months. But, because the last 3 months didn’t include a major holiday, you decide it is better to look at a full year’s worth of data. As you suspected, the quantitative data confirmed that average attendance was 550 per month but then rose to an average of 1,600 per month for the months with holidays. The historical data serves your needs for the project, but you also decide that you will resume the analysis again in a few months after the theater increases ticket prices for evening showtimes. **Profitability of the concession stand** Image of a stack of money and coins. There is a clock in the background Profit is calculated by subtracting cost from sales revenue. The historical data shows that while the concession stand was profitable, profit margins were razor thin at less than 5%. You saw that average purchases totaled $20 or less. You decide that you will keep monitoring this on an ongoing basis. Based on your [understanding of data](https://analyticsn.com/data-vs-metrics-understanding-the-core-difference/) collection tools, you will suggest an online survey of customers so they can comment on the food at the concession stand. This will enable you to gather even more quantitative data to revamp the menu and potentially increase profits. **Evening audience preferences** Image of a person sitting across a group of people Your [analysis of the historical data](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) shows that the 7:30 PM showtime was the most popular and had the greatest attendance, followed by the 7:15 PM and 9:00 PM showtimes. You may suggest replacing the current 8:00 PM showtime that has lower attendance with an 8:30 PM showtime. But you need more data to back up your hunch that people would be more likely to attend the later show. Evening movie-goers are the largest source of revenue for the theater. Therefore, you also decide to include a question in your online survey to gain more insight. **Qualitative data for all three trends plus ticket pricing** Since you know that the theater is planning to raise ticket prices for evening showtimes in a few months, you will also include a question in the survey to get an idea of customers’ price sensitivity. - Your final online survey might include these questions for qualitative data: - What went into your decision to see a movie in our theater today? (movie attendance) - What do you think about the quality and value of your purchases at the concession stand? (concession stand profitability) - Which showtime do you prefer, 8:00 PM or 8:30 PM, and why do you prefer that time? (evening movie-goer preferences) - Under what circumstances would you choose a matinee over a nighttime showing? (ticket price increase) **Key takeaways** Data [analysts will generally use both types of data](https://analyticsn.com/?p=1512) in their work. Usually, qualitative data can help analysts better understand their quantitative data by providing a reason or more thorough explanation. In other words, quantitative data generally gives you the what, and qualitative data generally gives you the why. By using both quantitative and qualitative data, you can learn when people like to go to the movies and why they chose the theater. Maybe they really like the reclining chairs, so your manager can purchase more recliners. Maybe the theater is the only one that serves root beer. Maybe a later show time gives them more time to drive to the theater from where popular restaurants are located. Maybe they go to matinees because they have kids and want to save money. You wouldn’t have discovered this information by analyzing only the quantitative data for attendance, profit, and showtimes. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Academic Research, Blog, Data Analysis **Tags:** AnalyticsN, concession stand, customer feedback, customer preferences, data analysis, data collection tools, evening audience preferences, historical data, insights, loyalty program, matinee, movie attendance, nighttime showing, price sensitivity, profitability, qualitative data, quantitative data, revenue analysis, showtime preferences, survey, ticket pricing --- ### [How to choose a statistical test for a hypothesis?](https://analyticsn.com/how-to-choose-a-statistical-test-for-a-hypothesis/) **Published:** January 23, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn Introduction to Hypothesis Testing and Statistical Tools The general process of hypothesis testing provides a foundation for evaluating research questions through inferential statistics. As you begin testing your own hypotheses, it is essential to understand that interpreting p-values remains a consistent aspect of all inferential tests. However, the specific statistical test you choose depends on Introduction to Hypothesis Testing and Statistical Tools The general process of hypothesis testing provides a foundation for evaluating research questions through inferential statistics. As you begin testing your own hypotheses, it is essential to understand that interpreting p-values remains a consistent aspect of all inferential tests. However, the specific statistical test you choose depends on](https://analyticsn.com/wp-content/uploads/2025/01/How-to-choose-a-statistical-test.png "How to choose a statistical test - Analyticsn")## Introduction to Hypothesis Testing and Statistical Tools The general process of hypothesis testing provides a foundation for evaluating research questions through inferential statistics. As you begin testing your own hypotheses, it is essential to understand that interpreting **[p-values](https://analyticsn.com/wp-content/uploads/2025/01/What-is-p-value-in-Statistics.png "What is p-value in Statistics")** remains a consistent aspect of all inferential tests. However, the specific statistical test you choose depends on the types of variables involved in your analysis. These tests help examine the relationship between two variables: an **explanatory variable** (independent variable) and a **response variable** (dependent variable). ## Understanding Bivariate Statistical Tools ![Analyticsn Introduction to Hypothesis Testing and Statistical Tools The general process of hypothesis testing provides a foundation for evaluating research questions through inferential statistics. As you begin testing your own hypotheses, it is essential to understand that interpreting p-values remains a consistent aspect of all inferential tests. However, the specific statistical test you choose depends on Introduction to Hypothesis Testing and Statistical Tools The general process of hypothesis testing provides a foundation for evaluating research questions through inferential statistics. As you begin testing your own hypotheses, it is essential to understand that interpreting p-values remains a consistent aspect of all inferential tests. However, the specific statistical test you choose depends on](https://analyticsn.com/wp-content/uploads/2025/01/image.png "image - Analyticsn")In [hypothesis testing,](https://analyticsn.com/from-sample-to-population-hypothesis-testing/) the term **bivariate** refers to analyses involving two variables. The choice of the appropriate statistical test depends on whether the explanatory and response variables are categorical or quantitative. Below are common scenarios and the corresponding inferential tests: ### 1. Categorical Explanatory Variable and Quantitative Response Variable When the explanatory variable is categorical, and the response variable is quantitative, the **Analysis of Variance (ANOVA)** is the appropriate inferential test. ANOVA evaluates whether there are significant differences in the means of the response variable across the levels of the explanatory variable. For example, if you are investigating the effect of different exercise regimens (categorical explanatory variable) on weight loss (quantitative response variable), ANOVA would determine whether the mean weight loss differs significantly across the exercise groups. ### 2. Categorical Explanatory Variable and Categorical Response Variable If both the explanatory and response variables are categorical, the **Chi-Square Test of Independence** is used. This test assesses whether there is an association between the two categorical variables. For instance, if you are examining whether gender (categorical explanatory variable) is related to smoking status (categorical response variable: smoker or non-smoker), the Chi-Square Test would evaluate the independence between these two variables. ### 3. Quantitative Explanatory Variable and Quantitative Response Variable When both the explanatory and response variables are quantitative, the appropriate inferential test is the **correlation coefficient**. This test measures the strength and direction of the relationship between the two variables. For example, if you are analyzing the relationship between study hours (quantitative explanatory variable) and exam scores (quantitative response variable), the correlation coefficient would quantify how closely the two variables are related. ### 4. Quantitative Explanatory Variable and Categorical Response Variable If the explanatory variable is quantitative and the response variable is categorical, the explanatory variable is typically categorized into two levels. After this categorization, the **Chi-Square Test of Independence** is used as the inferential test. For instance, if you are studying the relationship between income (quantitative explanatory variable) and voting preference (categorical response variable: Candidate A or Candidate B), you might divide income into two categories (e.g., below or above a certain threshold) and apply the Chi-Square Test. ## Application in Research Understanding the appropriate bivariate [statistical tool for your variables ensures accurate hypothesis testing](https://analyticsn.com/post-hoc-tests-for-anova-in-statistics/) and meaningful results. By aligning the test to the nature of your explanatory and response variables, you can draw valid inferences about relationships in your data, supported by robust statistical evidence. This alignment is critical for ensuring that the test results appropriately address your research question. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Hypothesis Testing with Variance and Correlation **Tags:** Analysis of Variance (ANOVA), bivariate, categorical, Chi-Square Test of Independence, correlation coefficient, exam scores, exercise regimens, explanatory variable, gender, hypothesis testing, income, Inferential tests, p-values, quantitative, research, response variable, smoking status, statistical evidence, statistical tools, study hours, voting preference, weight loss --- ### [General Steps in Hypothesis Testing](https://analyticsn.com/general-steps-in-hypothesis-testing/) **Published:** January 16, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn Introduction to Hypothesis Testing Hypothesis testing is a fundamental tool in inferential statistics, used to make decisions about populations based on sample data. It involves assessing whether there is sufficient evidence in the data to support or refute a hypothesis about a population parameter. This process is crucial in real-world applications where direct examination of Introduction to Hypothesis Testing Hypothesis testing is a fundamental tool in inferential statistics, used to make decisions about populations based on sample data. It involves assessing whether there is sufficient evidence in the data to support or refute a hypothesis about a population parameter. This process is crucial in real-world applications where direct examination of Steps in Hypothesis Testing](https://analyticsn.com/wp-content/uploads/2025/01/Steps-in-Hypothesis-Testing.png "Steps in Hypothesis Testing - Analyticsn")## Introduction to Hypothesis Testing Hypothesis testing is a fundamental tool in inferential statistics, used to make decisions about populations based on sample data. It involves assessing whether there is sufficient evidence in the data to support or refute a hypothesis about a population parameter. This process is crucial in real-world applications where direct examination of entire populations is impractical. Various statistical tests, such as the [Analysis of Variance (ANOVA)](https://analyticsn.com/wp-content/uploads/2025/01/Significance-of-Statistical-Inference-ANOVA.png "Significance of Statistical Inference – ANOVA") and the Chi-Square Test of Independence, are employed in hypothesis testing, but they all follow the same essential steps. These steps include specifying the null and alternative hypotheses, choosing a sample, assessing evidence, and drawing conclusions. ## Step 1: Formulating Hypotheses The first step in hypothesis testing is to define the **null hypothesis** (H0H\_0H0​) and the **alternative hypothesis** (HaH\_aHa​). The null hypothesis typically states that there is no effect or no difference in the parameter of interest. In contrast, the alternative hypothesis posits that there is an effect or difference. For example, consider the relationship between depression and smoking behavior. The null hypothesis states that there is no difference in smoking quantity between individuals with and without depression. The alternative hypothesis suggests that there is a difference, which could manifest as smokers with depression either consuming more or fewer cigarettes than those without depression. ## Step 2: Selecting a Sample The next step involves selecting a [sample from the population to test](https://analyticsn.com/from-sample-to-population-hypothesis-testing/) the hypotheses. In this example, data from the NESARC dataset, a representative sample of 43,093 U.S. adults, is used. To focus the analysis, the sample is narrowed down to young adults aged 18–25 who are daily smokers, resulting in a subset of 1,320 individuals. Among this group, individuals with depression smoked an average of 13.9 cigarettes per day with a standard deviation of 9.2, while those without depression smoked an average of 13.2 cigarettes per day with a standard deviation of 8.5. While the average for those with depression is slightly higher, this observed difference may not be significant enough to reject the null hypothesis. ## Step 3: Assessing the Evidence This [step involves evaluating the data](https://analyticsn.com/10-meet-wide-and-long-data-step-by-step/) to determine whether the observed difference between groups is significant or could have occurred by chance. The key question is whether the difference of 0.7 cigarettes per day between smokers with and without depression is sufficiently unusual under the assumption that the null hypothesis is true. This is assessed by calculating the probability of observing such a difference due to random variation alone, a measure known as the p-value. In this example, the probability of observing a difference of this magnitude or greater, assuming the null hypothesis is true, is approximately 0.17 (17%). This means that if we repeatedly took random samples from the population, about 17 out of 100 samples would show a difference of 0.7 cigarettes per day purely by chance. ## Step 4: Making a Decision The final step is deciding whether to reject or fail to reject the null hypothesis based on the calculated probability. A p-value of 0.17 indicates a relatively high chance of observing the difference by random variation, which weakens the evidence against the null hypothesis. In [hypothesis testing,](https://analyticsn.com/understanding-the-two-sample-test-a-key-tool-in-hypothesis-testing/) the threshold for rejecting the null hypothesis, known as the significance level (α\\alphaα), is often set at 0.05 (5%). This means that a p-value below 0.05 would lead to rejecting the null hypothesis, while a p-value above 0.05 would result in failing to reject it. In this case, with a p-value of 0.17, there is insufficient evidence to confidently reject the null hypothesis. This means that while the data suggest a difference in smoking quantity, it is not [statistically significant](https://analyticsn.com/significance-of-statistical-inference-anova/) at the 5% level. Researchers must also consider the trade-offs involved in making errors. A higher p-value, such as 0.50, would suggest complete uncertainty akin to flipping a coin, while a smaller p-value, such as 0.05 or 0.01, provides stronger confidence in rejecting the null hypothesis. ## Conclusion and Guidelines for Decision-Making Hypothesis testing is a structured process that helps quantify uncertainty and make informed decisions about population parameters. The decision to reject or fail to reject the null hypothesis hinges on the p-value and its comparison with a predetermined significance level. While a p-value of 0.17 might not be compelling enough to reject the null hypothesis, lower probabilities, such as 0.05 or 0.01, provide stronger grounds for making such decisions. Establishing clear thresholds and understanding the implications of errors are essential for drawing reliable conclusions in statistical research. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Hypothesis Testing with Variance and Correlation **Tags:** alternative hypothesis, Analysis of Variance (ANOVA), Chi-Square Test, hypothesis testing, inferential statistics, null hypothesis, p-value, population parameters, significance level, statistical tests --- ### [From sample to population - Hypothesis Testing](https://analyticsn.com/from-sample-to-population-hypothesis-testing/) **Published:** January 13, 2025 **Author:** AnalyticsN **Excerpt:** The relationship between sample and population is key in statistics. Samples allow researchers to estimate population characteristics without studying everyone. Variations between samples, called sampling variability, are natural but can be managed using concepts like the Central Limit Theorem, enabling accurate inferences about populations. **Content:** ![Analyticsn The relationship between sample and population is key in statistics. Samples allow researchers to estimate population characteristics without studying everyone. Variations between samples, called sampling variability, are natural but can be managed using concepts like the Central Limit Theorem, enabling accurate inferences about populations. The relationship between sample and population is key in statistics. Samples allow researchers to estimate population characteristics without studying everyone. Variations between samples, called sampling variability, are natural but can be managed using concepts like the Central Limit Theorem, enabling accurate inferences about populations.](https://analyticsn.com/wp-content/uploads/2025/01/From-Sample-to-Population-Hypothesis-Testing.png "From Sample to Population - Hypothesis Testing - Analyticsn")## Understanding the Relationship Between Sample and Population The relationship between a sample and population is fundamental to statistical analysis. In research, the population represents the entire group of interest, while the sample is a subset selected for study. Sampling allows researchers to make inferences about the population without needing to [collect data](https://analyticsn.com/1-data-collection-in-todays-world/) from every individual. However, sample results are often not identical to population parameters due to variability. This natural variation, known as **sampling variability**, occurs even with random samples and is a key concept in understanding how sample statistics relate to population parameters. For example, consider the distribution of blood types in the U.S. population, where Type A and Type O are common, and AB and B are less common. If we take a random sample of 500 individuals, the percentages of each blood type in the sample may slightly differ from the population percentages. This difference occurs because the sample is only a fraction of the population. A second sample of 500 individuals will also yield different results, further illustrating sampling variability. Despite these differences, random samples are expected to approximate the population’s characteristics, provided they are large and unbiased. ## Sampling Variability in Quantitative Data Sampling variability also applies to continuous variables, such as height. In the U.S., the heights of adult males follow a normal distribution with a mean of 69 inches and a standard deviation of 2.8 inches. When a sample of 200 adult males is selected, the sample’s mean and standard deviation may differ slightly from the population values. For instance, one sample might yield a mean height of 68.7 inches and a standard deviation of 2.95 inches, while another sample produces a mean of 69.065 inches and a standard deviation of 2.659 inches. These variations between samples are expected and reflect the natural fluctuations inherent in sampling. To visualize this relationship, consider the histograms of these samples. While each sample’s distribution resembles the normal distribution of the population, the specific statistics differ slightly. Sampling variability ensures that no two samples from the same population are identical, but they collectively provide valuable insights into population parameters. ## Parameters and Statistics: Key Concepts Parameters are numerical values that describe the entire population, such as the population mean or standard deviation. In contrast, [statistics are values calculated](https://analyticsn.com/calculating-central-tendency-in-spss-statistics-stepwise-explanation/) from samples, such as the sample mean or standard deviation. While parameters are typically unknown due to the impracticality of studying every member of a large population, statistics are used to estimate these parameters. Because statistics vary from sample to sample, researchers rely on statistical methods to account for sampling variability and make informed inferences about the population. In the example of male heights, the population parameter is the mean height of 69 inches. However, each sample provides its own statistic, such as the sample mean of 68.7 inches or 69.065 inches. These sample statistics differ slightly due to variability, but they serve as approximations of the population parameter. ## The Central Limit Theorem and Sampling Distributions The Central Limit Theorem (CLT) explains how sample statistics behave across multiple samples. According to the CLT, if sufficiently large and numerous samples are drawn from a population, the distribution of their statistics (such as the mean or proportion) will approximate a normal distribution, regardless of the population’s original distribution. This theorem forms the basis for many inferential statistical methods. To illustrate, imagine selecting 30 random samples of 500 individuals from the U.S. population. Each sample would have its own mean height, which could be plotted on a bar graph. As more sample means are added, a pattern emerges: most sample means cluster near the population mean, while fewer fall at the extremes. This normal distribution of sample statistics is a hallmark of the Central Limit Theorem. ## Application of Sampling and Inference Inferential statistics rely on the relationship between sample and population to draw conclusions about the latter based on the former. A representative sample allows researchers to estimate population parameters with varying degrees of certainty. For example, if a sample of U.S. adults is used to estimate the population’s mean height, inferential methods account for [sampling variability and provide confidence intervals or significance tests](https://analyticsn.com/understanding-paired-sample-test-a-key-tool-for-comparative-analysis/) to support the findings. Learn more about [Statistical Inference](https://analyticsn.com/wp-content/uploads/2025/01/Significance-of-Statistical-Inference-ANOVA.png "Significance of Statistical Inference – ANOVA") This framework enables [researchers to answer complex](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/) questions about populations using manageable and practical samples, forming the foundation of statistical inquiry and analysis. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Hypothesis Testing with Variance and Correlation **Tags:** Central Limit Theorem, confidence intervals, inferential statistics, normal distribution, population parameters, representative sample, sample and population, sample statistics, sampling variability, statistical analysis --- ### [Significance of Statistical Inference (ANOVA)](https://analyticsn.com/significance-of-statistical-inference-anova/) **Published:** January 9, 2025 **Author:** AnalyticsN **Excerpt:** Statistical inference helps us make sense of data and draw conclusions about larger groups by analyzing smaller samples. It uses descriptive and inferential methods to find patterns, test ideas, and predict outcomes. Probability plays a key role by helping us handle uncertainty and make better decisions based on data. **Content:** ![Analyticsn Statistical inference helps us make sense of data and draw conclusions about larger groups by analyzing smaller samples. It uses descriptive and inferential methods to find patterns, test ideas, and predict outcomes. Probability plays a key role by helping us handle uncertainty and make better decisions based on data. Statistical inference helps us make sense of data and draw conclusions about larger groups by analyzing smaller samples. It uses descriptive and inferential methods to find patterns, test ideas, and predict outcomes. Probability plays a key role by helping us handle uncertainty and make better decisions based on data.](https://analyticsn.com/wp-content/uploads/2025/01/Significance-of-Statistical-Inference-ANOVA.png "Significance of Statistical Inference - ANOVA - Analyticsn")## Understanding Statistical Inference Statistical inquiry begins with organizing and summarizing data to extract meaningful patterns and insights. This process involves **exploratory data analysis**, which includes examining frequency distributions, creating graphical representations of variables, and calculating measures of central tendency and spread. These steps help identify patterns, important features, and deviations in the data. This phase is referred to as **Descriptive Statistics**, which aims to summarize a sample of data quantitatively, providing a clear overview of the dataset without making predictions or generalizations. ## Transitioning to Inferential Statistics While descriptive [statistics focus on summarizing data](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/), **Inferential Statistics** takes a broader step by testing hypotheses and drawing conclusions about a larger population based on a sample. This process allows researchers to evaluate their research questions with the aim of generalizing findings to the [population from which the sample](https://analyticsn.com/from-sample-to-population-hypothesis-testing/) was drawn. A [key tool](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/) in this domain is **Hypothesis Testing**, which evaluates evidence supporting or opposing hypotheses about the population. Hypothesis testing is essential for addressing real-world problems, such as estimating population parameters or making decisions based on data. ## The Foundation of Probability in Statistical Inference Probability forms the backbone of inferential statistics. When working with random samples, it is critical to understand that no sample perfectly represents the population, as randomness introduces variation. Probability quantifies this uncertainty, allowing researchers to assess how much a random [sample might deviate from the population](https://analyticsn.com/from-sample-to-population-hypothesis-testing/) it represents. It provides a framework for estimating the likelihood that [sample results are accurate representations of the population,](https://analyticsn.com/from-sample-to-population-hypothesis-testing/) enabling informed decision-making even in the face of uncertainty. For example, consider a study aiming to estimate the percentage of U.S. adults who favor the death penalty. A random sample of 1,200 individuals might reveal that 62% support it. However, since the results are based on a random sample, they are subject to uncertainty. Probability helps quantify the likelihood that the true population percentage lies within a specific range, such as within 3% of the sample estimate. This capability underscores the role of probability in providing confidence to inferences drawn from sample data. ## Exploring the Concept of Probability Probability is a measure of the likelihood of an event occurring. It provides a mathematical description of randomness and uncertainty, making it an essential [tool for interpreting statistical](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/) results. Probability values range from 0 to 1, where: - A probability of 0 means the event will never occur. - A probability of **1** means the event will certainly occur. - A probability of **0.5** indicates an equal likelihood of the event occurring or not. Probability can also be expressed as percentages, ranging from 0% to 100%. These values help quantify how likely an event is to occur, providing a clearer understanding of statistical outcomes. ## Practical Applications of Probability The gambling industry serves as a vivid example of probability in action. Casinos leverage probability to design games where the odds are in their Favor, while gamblers rely on chance. Probability calculations in games of chance illustrate concepts like independent and dependent events. For instance: - The chance of drawing a red card from a shuffled deck is 50%, as half of the cards are red. - Sequential events, such as drawing a king, removing it from the deck, and then drawing another king, demonstrate how probabilities change with each event. These scenarios showcase the practical use of probability in understanding and predicting outcomes in uncertain environments. By defining probability and demonstrating its application in statistical inference and real-world examples, the importance of this foundational concept becomes clear. Probability allows researchers to navigate uncertainty, quantify variation, and draw meaningful conclusions from data, making it a cornerstone of statistical practice. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Hypothesis Testing with Variance and Correlation **Tags:** ANOVA, descriptive statistics, inferences, qualitative data, regression analysis excel, secondary data analysis, statistical methods --- ### [Post-hoc tests for ANOVA in Statistics](https://analyticsn.com/post-hoc-tests-for-anova-in-statistics/) **Published:** January 30, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn Introduction to Post-Hoc Tests in ANOVA When an Analysis of Variance (ANOVA) reveals significant differences across groups for an explanatory variable with more than two categories, it does not specify which groups differ from each other. To determine the specific group differences, a post-hoc test is required. Post-hoc, meaning "after the fact," involves paired comparisons Introduction to Post-Hoc Tests in ANOVA When an Analysis of Variance (ANOVA) reveals significant differences across groups for an explanatory variable with more than two categories, it does not specify which groups differ from each other. To determine the specific group differences, a post-hoc test is required. Post-hoc, meaning "after the fact," involves paired comparisons ost-hoc tests for ANOVA](https://analyticsn.com/wp-content/uploads/2025/01/Post-hoc-tests-for-ANOVA.png "Post-hoc tests for ANOVA - Analyticsn")## Introduction to Post-Hoc Tests in ANOVA When an Analysis of Variance (ANOVA) reveals significant differences across groups for an explanatory variable with more than two categories, it does not specify which groups differ from each other. To determine the specific group differences, a **post-hoc test** is required. Post-hoc, meaning “after the fact,” involves paired comparisons of group means. These comparisons are conducted in a controlled manner to minimize the risk of **Type I errors**, which occur when the null hypothesis is incorrectly rejected. ## Why Not Use Multiple ANOVAs? Performing multiple ANOVAs to compare all possible group pairs might seem like an intuitive approach. However, each[ ANOVA](https://analyticsn.com/wp-content/uploads/2025/01/Significance-of-Statistical-Inference-ANOVA.png "Significance of Statistical Inference – ANOVA") carries a 5% chance of committing a Type I error if the significance level is set at p≤0.05p \\leq 0.05p≤0.05. Conducting multiple tests inflates this error rate, known as the **family-wise error rate**, leading to a much higher overall likelihood of rejecting the null hypothesis when it is true. For instance, after 10 tests, the probability of committing a Type I error rises to 40%, making the results less reliable. ## Purpose of Post-Hoc Tests Post-hoc tests are specifically designed to compare pairs of means while controlling for the inflation of Type I errors. They adjust the error rate to ensure that the likelihood of incorrect conclusions remains within acceptable bounds. Several post hoc tests are available, including: - Sidak Test - Holm Test - Fisher’s Least Significant Difference Test - Tukey’s Honestly Significant Difference Test - Scheffé Test - Newman-Keuls Test - Dunnett’s Multiple Comparison Test - Duncan Multiple Range Test - Bonferroni Procedure While these tests vary in their level of conservatism (how strictly they control for Type I errors), the choice of test is often less critical than ensuring that a post hoc analysis is conducted. ## Applying Post-Hoc Analysis: An Example In examining the relationship between ethnicity and the number of cigarettes smoked per month among young adult smokers, the Duncan Multiple Range Test was used as the post hoc method. This test was applied following an ANOVA with a [significant F statistic](https://analyticsn.com/significance-of-statistical-inference-anova/) (F=24.4F = 24.4F=24.4) and p<0.0001p < 0.0001p<0.0001, indicating significant differences among groups. ## Interpreting Duncan Post-Hoc Results The results of the Duncan Test are displayed in a table where group means with the same capital letter next to them are not significantly different. For example: - Ethnic groups 1 (White) and 3 (American Indian/Alaskan Native) share the letter “A,” indicating no significant difference between them. - Groups 2 (Black), 4 (Asian), and 5 (Hispanic/Latino) share the letter “C,” meaning they are not significantly different from each other. - Groups 3, 2, and 4 also share the letter “B,” showing no significant differences among these groups. Significant differences were observed in the following cases: - Group 1 (White) smoked significantly more cigarettes per month than groups 2 (Black), 4 (Asian), and 5 (Hispanic/Latino). - Group 3 (American Indian/Alaskan Native) smoked significantly more per month than group 5 (Hispanic/Latino). ## Important Considerations in Interpretation Some means have more than one letter next to them, indicating overlapping groups. It is crucial to follow the rule that means sharing at least one letter are not significantly different from one another. This nuanced approach ensures accurate identification of significant differences while maintaining rigorous control over Type I error rates. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Hypothesis Testing with Variance and Correlation **Tags:** ANOVA, Bonferroni Procedure, cigarettes smoked, Duncan Multiple Range Test, Duncan Post Hoc Results, Dunnett’s Multiple Comparison Test, error rate control, ethnicity, family-wise error rate, Fisher’s Least Significant Difference, group means, Holm Test, interpretation, Newman-Keuls Test, paired comparisons, post hoc tests, Scheffé Test, Sidak Test, significant differences, statistical analysis, Tukey’s Honestly Significant Difference, Type I Error --- ### [Mastering Data Analysis with Mathematical Thinking: A Guide to Small and Big Data Solutions](https://analyticsn.com/mastering-data-analysis-with-mathematical-thinking-a-guide-to-small-and-big-data-solutions/) **Published:** October 7, 2024 **Author:** AnalyticsN **Excerpt:** mathematical thinking enhances data analysis by breaking down problems, identifying patterns, and choosing the right tools, from spreadsheets for small data to SQL for big data. Discover practical insights for data-driven decision-making in various contexts. **Content:** ![Analyticsn mathematical thinking enhances data analysis by breaking down problems, identifying patterns, and choosing the right tools, from spreadsheets for small data to SQL for big data. Discover practical insights for data-driven decision-making in various contexts. mathematical thinking enhances data analysis by breaking down problems, identifying patterns, and choosing the right tools, from spreadsheets for small data to SQL for big data. Discover practical insights for data-driven decision-making in various contexts. Mastering Data Analysis with Mathematical Thinking: A Guide to Small and Big Data Solutions](https://analyticsn.com/wp-content/uploads/2024/10/Mathematical-Thinking.jpg "Mathematical Thinking - Analyticsn")So far, you’ve learned a lot about how to think like a data analyst in previous posts. We’ve explored a few different ways of thinking. And now, I want to take that one step further by using a mathematical approach to problem-solving. Mathematical thinking is a powerful skill you can use to help you solve problems and see new solutions. ## What is Mathematical Thinking? So, let’s take some time to talk about what mathematical thinking is, and how you can start using it. Using a mathematical approach doesn’t mean you have to become a math whiz suddenly. It means looking at a problem and logically breaking it down step-by-step, so you can see the relationship of patterns in your data, and use that to analyze your problem. This kind of thinking can also help you figure out the best [tools for analysis](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/) because it lets us see the different aspects of a problem and choose the best logical approach. There are a lot of factors to consider when [choosing the most helpful tool for your analysis](https://analyticsn.com/how-to-choose-the-right-descriptive-analysis-in-spss/). One way you could decide which tool to use is by the size of your dataset. When working with data, you’ll find that there’s big and small data. ### Small Data Small data can be really small. These kinds of data tend to be made up of datasets concerned with specific metrics over a short, well defined period of time. Like how much water you drink in a day. Small [data can be useful for making day-to-day decisions, like](https://analyticsn.com/how-data-empowers-decision-in-data-analytics/) deciding to drink more water. But it doesn’t have a huge impact on bigger frameworks like business operations. You might use spreadsheets to organize and analyze smaller datasets when you first start out. ### Big Data Big data on the other hand has larger, less specific datasets covering a longer period of time. They usually have to be broken down to be analyzed. Big data is useful for looking at large- scale questions and problems, and they help companies make big decisions. When you’re [working with data](https://analyticsn.com/6-know-the-data-type-youre-working-with/) on this larger scale, you might switch to SQL. Let’s look at an example of how a data analyst working in a hospital might use mathematical thinking to solve a problem with the right tools. The hospital might find that they’re having a problem with over or under use of their beds. Based on that, the hospital could make bed optimization a goal. They want to make sure that beds are available to patients who need them, but not waste hospital resources like space or money on maintaining empty beds. Using mathematical thinking, you can break this problem down into a step-by-step process to help you find patterns in their data. There’s a lot of variables in this scenario. But for now, let’s keep it simple and focus on just a few key ones. There are metrics that are related to this problem that might show us patterns in the data: for example, maybe the number of beds open and the number of beds used over a period of time. There’s actually already a formula for this. It’s called the bed occupancy rate, and it’s calculated using the total number of inpatient days, and the total number of available beds over a given period of time. What we want to do now is take our key variables and see how their relationship to each other might show us patterns that can help the hospital make a decision. To do that, we have to choose the tool that makes sense for this task. Hospitals generate a lot of patient data over a long period of time. So logically, a tool that’s capable of handling big datasets is a must. **SQL is a great choice. In this case, you discover that the hospital always has unused beds. Knowing that, they can choose to get rid of some beds, which saves them space and money that they can use to buy and store protective equipment. By considering all of the individual parts of this problem logically, mathematical thinking helped us see new perspectives that led us to a solution. You’ve learned about how [empowering data can be in decision-making](https://analyticsn.com/how-data-empowers-decision-in-data-analytics/ "empowering data can be in decision-making"), [the difference between quantitative and qualitative analysis](https://analyticsn.com/qualitative-and-quantitative-data/ "the difference between quantitative and qualitative analysis"), and [using reports and dashboards for data visualization](https://analyticsn.com/design-compelling-dashboards-with-tableau-for-data-analysis-and-stakeholders/ "using reports and dashboards for data visualization"), and much more. There is a lot remained to learn in the upcoming articles. Stay tuned. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Blog, Data Analysis **Tags:** AnalyticsN, Bed Occupancy Rate, Big Data, data analysis, Data Analysis Tools, Data analytics, Data patterns, Data Visualization, Data-Driven Decision Making, Data-Driven Solutions, Hospital Data Management, Mathematical Thinking, Quantitative Analysis, Small Data, Spreadsheet Basics, SQL --- ### [Data vs. Metrics: Understanding the Core Difference](https://analyticsn.com/data-vs-metrics-understanding-the-core-difference/) **Published:** September 4, 2024 **Author:** AnalyticsN **Excerpt:** Understanding the difference between data vs. metrics is crucial for turning raw facts into actionable insights. While data consists of unorganized information, metrics are quantifiable measurements that provide context and help track performance. By converting data into metrics, businesses can make informed decisions and drive success. **Content:** ![Analyticsn Understanding the difference between data vs. metrics is crucial for turning raw facts into actionable insights. While data consists of unorganized information, metrics are quantifiable measurements that provide context and help track performance. By converting data into metrics, businesses can make informed decisions and drive success. Understanding the difference between data vs. metrics is crucial for turning raw facts into actionable insights. While data consists of unorganized information, metrics are quantifiable measurements that provide context and help track performance. By converting data into metrics, businesses can make informed decisions and drive success. DATA vs METRICS - understanding the core differences - analyticsn.com](https://analyticsn.com/wp-content/uploads/2024/09/DATA-vs-METRICS.jpg "DATA vs METRICS - Analyticsn")## Introduction In today’s data-driven world, organizations rely heavily on information to make informed decisions and improve performance. However, when it comes to understanding the foundational elements of data analysis, distinguishing between *data vs. metrics* is critical. While both terms are often used interchangeably, they serve different purposes in the process of gathering, organizing, and interpreting information. Data represents raw, unstructured facts, whereas metrics are the measurable, calculated values derived from that data to provide context and meaning. Understanding the differences between *data vs. metrics* is key to leveraging information [effectively for strategic decisions and business](https://analyticsn.com/data-driven-vs-data-inspired-decision-making-balancing-for-effective-business-strategy/) success. ### **Understanding the Difference Between Data and Metrics** (Data vs. Metrics) In the modern world, data and metrics play a crucial role in shaping decisions, strategies, and business outcomes. While these terms are often used interchangeably, they represent different aspects of information processing that are fundamental to effective analysis. #### Data **Data** refers to raw facts and details that, on their own, remain unorganized and lack context. This can include numbers, observations, or any factual input that hasn’t yet been processed. Data alone doesn’t provide much value until it is structured or given meaning. #### Metrics On the other hand, **metrics** are quantifiable measurements derived from organizing data using specific formulas or calculations. They help convert raw data into meaningful insights, which organizations can use to track performance, set goals, or make decisions. Metrics bring context and clarity to data, offering a more structured way to measure success and progress. Here’s a comparison table that highlights the key differences between **Data** and **Metrics**: **Aspect****Data****Metrics****Definition**Raw, unprocessed facts or figuresQuantifiable measurements derived from data**Purpose**Provides foundational informationProvides insights by organizing and interpreting data**Context**Lacks context and meaning when isolatedAdds context through specific calculations or formulas**Use**Collected from various sources for potential analysisUsed to track, measure, and evaluate performance or trends**Example**Individual sales figures of a productAverage sales per month or percentage growth over time**Nature**Unstructured and requires processingStructured and results from analysis or calculations**Role in Analytics**The raw input needed to create metricsThe output that provides insights for decision-making**Actionability**Not directly actionable in its raw formDirectly actionable, offering clear benchmarks or goalsThis [table helps clarify how **data**](https://analyticsn.com/8-data-table-components-a-quick-overview/) serves as the foundation for **metrics**, which in turn, provide actionable insights for effective decision-making. ## Need Help? We provide expert data analysis service at affordable rates. [Schedule Your FREE Consultation](https://analyticsn.com#contact-form-section) ### Why the Distinction Matters The distinction between data and metrics is important for anyone involved in analytics. Data is the starting point of any analysis, but without organizing it into metrics, it’s like having pieces of a puzzle without the final picture. Metrics provide the benchmarks needed for comparison and tracking, turning raw [data into actionable](https://analyticsn.com/from-issue-to-action-the-six-data-analysis-phases/) intelligence. In industries such as business, education, and healthcare, proper use of data and metrics can drive productivity and innovation. Companies rely on metrics to evaluate performance and identify areas for improvement, while data offers the foundational input that feeds these metrics. For expert insights and comprehensive data analysis services, [contact us](https://analyticsn.com/#contact-us) ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Academic Research, Blog, Data Analysis **Tags:** Actionable insights, Business performance, Business success, data analysis, Data Interpretation, Data Organization, Data vs Metrics comparison., Data vs. Metrics, Data-driven Decisions, ddata, Information processing, metrics, Metrics analysis, Performance tracking, Quantifiable measurements, Raw data, Strategic decisions --- ### [Calculating Central Tendency in SPSS Statistics: Stepwise Explanation](https://analyticsn.com/calculating-central-tendency-in-spss-statistics-stepwise-explanation/) **Published:** March 15, 2024 **Author:** AnalyticsN **Excerpt:** Learn how to calculate central tendency measures like mean, median, and mode in SPSS Statistics. This step-by-step guide simplifies the process, making it easy for beginners to perform statistical analysis. **Content:** ![Analyticsn Learn how to calculate central tendency measures like mean, median, and mode in SPSS Statistics. This step-by-step guide simplifies the process, making it easy for beginners to perform statistical analysis. Learn how to calculate central tendency measures like mean, median, and mode in SPSS Statistics. This step-by-step guide simplifies the process, making it easy for beginners to perform statistical analysis.](https://analyticsn.com/wp-content/uploads/2024/03/Calculating-Central-Tendency-in-SPSS-Stepwise.png "Calculating Central Tendency in SPSS Stepwise - Analyticsn")Central tendency is a statistical measure that represents the center or average value of a dataset. It provides valuable insights into the typical or representative value of a set of observations. SPSS Statistics is a powerful software tool commonly used for data analysis in various fields. In this tutorial, we will discuss the stepwise process of calculating central tendency using SPSS Statistics. ## Step 1: Launching SPSS Statistics To begin, open the SPSS Statistics software on your computer. Once launched, you will be presented with the SPSS Statistics main window. ## Step 2: Importing the Dataset Next, import the dataset you wish to analyze. Click on “File” in the menu bar and select “Open” to locate and open your dataset file. SPSS Statistics supports various file formats, including Excel, CSV, and SPSS data files. ## Step 3: Selecting the Variable After importing the dataset, you need to choose the variable for which you want to calculate central tendency. In the “Variable View” tab, you will see a list of variables in your dataset. Locate the variable of interest and note its name. ## Step 4: Descriptive Statistics Now, go to the “Analyse” menu and select “Descriptive Statistics”. A drop-down menu will appear, and you need to choose “Descriptives”. ## Step 5: Selecting the Variable for Analysis In the “Descriptives” dialog box, you will find a list of variables in your dataset. Select the variable you identified in Step 3 by clicking on it and then click the arrow button to move it to the “Variables” box. This indicates that you want to calculate central tendency for this variable. ## Step 6: Choosing Central Tendency Measures In the “Descriptives” dialog box, you will see a list of statistics options. To calculate central tendency, select the measures you are interested in, such as mean, median, and mode. You can select multiple measures by holding down the Ctrl key (or Command key on Mac) while clicking. ## Step 7: Running the Analysis Once you have selected the central tendency measures, click the “OK” button to run the analysis. SPSS Statistics will generate the output in a new window. ## Step 8: Interpreting the Results The output window will display the calculated central tendency measures for the selected variable. The mean represents the average value, the median represents the middle value, and the mode represents the most frequently occurring value in the dataset. ## Step 9: Further Analysis SPSS Statistics provides additional options for analyzing central tendency. You can explore other statistical measures, such as quartiles and percentiles, to gain a more comprehensive [understanding of your data](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/). These options are available in the “Descriptives” dialog box under the “Options” button. ## Step 10: Saving the Output If you want to save the output for future reference or reporting, go to the “File” menu and select “Save As”. Choose a location on your computer and provide a name for the output file. SPSS Statistics allows you to save the output in various formats, such as HTML, PDF, and Excel. ## Conclusion Calculating central tendency is a fundamental step in data analysis, and SPSS Statistics simplifies this process by providing a user-friendly interface. By following the stepwise explanation outlined in this blog post, you can easily calculate central tendency measures for your dataset using SPSS Statistics. Understanding the central tendency of your data is crucial for making informed decisions and drawing meaningful conclusions from your analysis. Remember, SPSS Statistics offers a wide range of statistical tools and techniques beyond central tendency. Exploring these options can enhance your [data analysis](https://analyticsn.com/from-issue-to-action-the-six-data-analysis-phases/) capabilities and provide deeper insights into your research or business objectives. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Blog, SPSS Statistics **Tags:** beginners, Calculating Central Tendency, central tendency, data analysis, mean, median, mode, SPSS Statistics, statistical analysis, step-by-step guide --- ### [Ask SMART Questions in Your Business Analysis](https://analyticsn.com/ask-smart-questions-in-your-business-analysis/) **Published:** August 9, 2024 **Author:** AnalyticsN **Excerpt:** In an ever-changing business environment, asking the right questions is crucial for sparking innovation and making data-driven decisions. SMART questions—Specific, Measurable, Action-oriented, Relevant, and Time-bound—help ensure that data analysis is effective and targeted. Avoiding vague, leading, and closed-ended questions enhances the quality of insights gathered. The right approach to questioning can transform data into actionable insights, tailored to various fields like retail, education, and small businesses. **Content:** ![Analyticsn How asking SMART questions drives better data analytics, business insights, and decision-making across retail, education, and corporate sectors. Elaborated with proven methods and real-world examples. In an ever-changing business environment, asking the right questions is crucial for sparking innovation and making data-driven decisions. SMART questions—Specific, Measurable, Action-oriented, Relevant, and Time-bound—help ensure that data analysis is effective and targeted. Avoiding vague, leading, and closed-ended questions enhances the quality of insights gathered. The right approach to questioning can transform data into actionable insights, tailored to various fields like retail, education, and small businesses. In an ever-changing business environment, asking the right questions is crucial for sparking innovation and making data-driven decisions. SMART questions—Specific, Measurable, Action-oriented, Relevant, and Time-bound—help ensure that data analysis is effective and targeted. Avoiding vague, leading, and closed-ended questions enhances the quality of insights gathered. The right approach to questioning can transform data into actionable insights, tailored to various fields like retail, education, and small businesses.](https://analyticsn.com/wp-content/uploads/2024/08/Lec-3.-Ask-SMART-Questions.png "Lec 3. Ask SMART Questions - Analyticsn")Companies in lots of industries today are dealing with rapid change and rising uncertainty. Even well-established businesses are under pressure to keep up with what is new and figure out what is next. To do that, they need to ask questions. Asking the right questions can help spark the innovative ideas that so many businesses are hungry for these days. Asking SMART questions is a key in any field especially in retail, education and business. The same goes for [data analytics](https://analyticsn.com/how-data-empowers-decision-in-data-analytics/). No matter how much information you have or how advanced your tools are, your data won’t tell you much if you don’t start with the right questions. Think of it like a detective with tons of evidence who doesn’t ask a key suspect about it. Coming up, you will learn more about how to ask highly effective questions, along with certain practices you want to avoid. Highly effective questions are SMART questions: ![Analyticsn How asking SMART questions drives better data analytics, business insights, and decision-making across retail, education, and corporate sectors. Elaborated with proven methods and real-world examples. In an ever-changing business environment, asking the right questions is crucial for sparking innovation and making data-driven decisions. SMART questions—Specific, Measurable, Action-oriented, Relevant, and Time-bound—help ensure that data analysis is effective and targeted. Avoiding vague, leading, and closed-ended questions enhances the quality of insights gathered. The right approach to questioning can transform data into actionable insights, tailored to various fields like retail, education, and small businesses.](https://analyticsn.com/wp-content/uploads/2024/08/image-7-1024x162.png "image-7 - Analyticsn")![Analyticsn How asking SMART questions drives better data analytics, business insights, and decision-making across retail, education, and corporate sectors. Elaborated with proven methods and real-world examples. In an ever-changing business environment, asking the right questions is crucial for sparking innovation and making data-driven decisions. SMART questions—Specific, Measurable, Action-oriented, Relevant, and Time-bound—help ensure that data analysis is effective and targeted. Avoiding vague, leading, and closed-ended questions enhances the quality of insights gathered. The right approach to questioning can transform data into actionable insights, tailored to various fields like retail, education, and small businesses.](https://analyticsn.com/wp-content/uploads/2024/08/image-8-1024x98.png "image-8 - Analyticsn")**Specific**: Is the question specific? Does it address the problem? Does it have context? Will it uncover a lot of the information you need?**Measurable**: Will the question give you answers that you can measure?**Action-oriented**: Will the answers provide information that helps you devise some type of plan?**Relevant**: Is the question about the particular problem you are trying to solve?**Time-bound**: Are the answers relevant to the specific time being studied?## Examples of SMART questions Here’s an example that breaks down the thought process of turning a problem question into one or more SMART questions using the SMART method: > What features do people look for when buying a new car? **Specific**: Does the question focus on a particular car feature? **Measurable**: Does the question include a feature rating system? **Action-oriented**: Does the question influence creation of different or new feature packages? **Relevant**: Does the question identify which features make or break a potential car purchase? **Time-bound**: Does the question validate data on the most popular features from the last three years? ***Questions should be open-ended*.** This is the best way to get responses that will help you accurately qualify or disqualify potential solutions to your specific problem. So, based on the thought process, possible SMART questions might be: - On a scale of 1-10 (with 10 being the most important) how important is your car having four-wheel drive? Explain. - What are the top five features you would like to see in a car package? - What features, if included with four-wheel drive, would make you more inclined to buy the car? - How does a car having four-wheel drive contribute to its value, in your opinion? **Things to avoid when asking questions** **Leading questions:** questions that only have a particular response **Example**: This product is too expensive, isn’t it? This is a leading question because it suggests an answer as part of the question. A better question might be, “What is your opinion of this product?” There are tons of answers to that question, and they could include information about usability, features, accessories, color, reliability, and popularity, on top of price. Now, if your problem is actually focused on pricing, you could ask a question like “What price (or price range) would make you consider purchasing this product?” This question would provide a lot of different measurable responses. ***Closed-ended questions*:** questions that ask for a one-word or brief response only **Example**: Were you satisfied with the customer trial? This is a closed-ended question because it doesn’t encourage people to expand on their answer. It is really easy for them to give one-word responses that aren’t very informative. A better question might be, “What did you learn about customer experience from the trial.” This encourages people to provide more detail besides “It went well.” ***Vague questions*:** questions that aren’t specific or don’t provide context **Example:** Does the tool work for you? This question is too vague because there is no context. Is it about comparing the new tool to the one it replaces? You just don’t know. A better inquiry might be, “When it comes to data entry, is the new tool faster, slower, or about the same as the old tool? If faster, how much time is saved? If slower, how much time is lost?” These questions give context (data entry) and help frame responses that are measurable (time). Use the SMART question framework to make sure each question you ask makes sense based on their field. Each question should meet as many of the SMART criteria as possible. **As a reminder, SMART questions are** **Specific**: Questions are simple, significant, and focused on a single topic or a few closely related ideas. **Measurable**: Questions can be quantified and assessed. **Action-oriented**: Questions encourage change. **Relevant**: Questions matter, are important, and have significance to the problem you’re trying to solve. **Time-bound**: Questions specify the time to be studied. For instance, if you have a conversation with someone who works in retail, you might lead with questions like: **Specific**: Do you currently use data to drive decisions in your business? If so, what kind(s) of [data do you collect](https://analyticsn.com/1-data-collection-in-todays-world/), and how do you use it? **Measurable**: Do you know what percentage of sales is from your top-selling products? **Action-oriented:** Are there [business decisions](https://analyticsn.com/data-driven-vs-data-inspired-decision-making-balancing-for-effective-business-strategy/) or changes that you would make if you had the right information? For example, if you had information about how umbrella sales change with the weather, how would you use it? **Relevant**: How often do you review data from your business? **Time-bound:** Can you describe how [data helped you make good decisions](https://analyticsn.com/how-data-empowers-decision-in-data-analytics/) for your store(s) this past year? If you are having a conversation with a teacher, you might ask different questions, such as: **Specific**: What kind of data do you use to build your lessons? **Measurable**: How well do student benchmark test scores correlate with their grades? **Action-oriented**: Do you share your data with other teachers to improve lessons? **Relevant**: Have you shared grading data with an entire class? If so, do students seem to be more or less motivated, or about the same? **Time-bound**: In the last five years, how many times did you review data from previous academic years? If you are having a conversation with a small business owner of an ice cream shop, you could ask: **Specific**: What data do you use to help with purchasing and inventory? **Measurable**: Can you order (rank) these factors from most to least influential on sales: price, flavor, and time of year (season)? **Action-oriented**: Is there a single [factor you need more data](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/) on so you can potentially increase sales? **Relevant**: How do you advertise to or communicate with customers? **Time-bound**: What does your year-over-year sales growth look like for the last three years? ## Take good notes It is important to take good notes during your conversation. Your notes should be comprehensive and useful. To help you capture meaningful notes, you should stick to a process of asking a question, clarifying your understanding of their response, and then briefly recording it in your notes. **Remember**: If a question is worth asking, then the answer is worth recording. Commit yourself to taking great notes during your conversation. **Helpful aspects of your conversation to note include:** ***Facts***: Write down any concrete piece of information, such as dates, times, names, and other specifics. ***Context***: Facts without context are useless. Note any relevant details that are needed in order to understand the information you gather. Unknowns: Sometimes you may miss an important question during a conversation. Make a note when this happens so you can figure out the answer later. ## **Why SMART Questions Matter in Data Analytics** Our **data analytics services** help businesses transform raw data into strategic insights, but this transformation only happens when we start with properly framed questions. Consider these real-world scenarios where SMART questions drive better analytics: ### **Retail Analytics Applications** - *“What specific product features drove a measurable 15% increase in sales last quarter?”* - *“Which customer segments show declining engagement, and what actions can we take to retain them?”* Our **retail analytics services** use these questions to: ✔ Optimize inventory based on predictive demand models ✔ Personalize marketing through customer segmentation ✔ Identify high-value store locations using geospatial analytics ### **Education Sector Insights** - *“How do student performance metrics correlate with specific teaching methods across semesters?”* - *“Which intervention programs delivered measurable improvements in graduation rates?”* Through our [**education data**](https://analyticsn.com/course-data-driven-visual-communication-in-an-educators-life/) services, we help institutions: ✔ Track learning outcomes with longitudinal analysis ✔ Allocate resources using cost-benefit modeling ✔ Predict at-risk students with early warning systems ### **Corporate Business Intelligence** - *“What operational factors most impact our profit margins, and how can we adjust them?”* - *“Which sales channels deliver the highest ROI when analyzed quarterly?”* Our **business analytics solutions** enable companies to: ✔ Streamline operations with process mining ✔ Forecast market trends using time-series analysis ✔ Benchmark performance against industry standards ## **From Questions to Action: Our Data Analytics Methodology** 1. **Discovery Workshop** We collaborate with clients to formulate SMART questions aligned with business objectives 2. **Data Assessment** Our experts evaluate available data sources and identify gaps 3. **Advanced Analysis** Using tools like **Python, R, Power BI, and Tableau**, we apply: - Predictive modeling - Customer sentiment analysis - Market basket analysis - Churn prediction models 4. **Actionable Reporting** We deliver clear, visualized insights with executive summaries and implementation roadmaps ## **Common Analytics Pitfalls We Help Avoid** Many organizations struggle with: ✖ **Vague Queries** *“Why are sales down?”* vs. *“Which product categories underperformed in Q2 compared to seasonal benchmarks?”* ✖ **Data Silos** We integrate disconnected [data sources into unified analytics](https://analyticsn.com/how-data-empowers-decision-in-data-analytics/) platforms ✖ **Analysis Paralysis** Our consultants focus on delivering decision-ready insights, not just data dumps ## **Case Study: Transforming Questions into Results** A regional retailer asked: *“How can we reduce inventory costs without impacting customer satisfaction?”* Our **supply chain analytics** revealed: - 22% of SKUs accounted for 83% of sales - Seasonal demand patterns for different product categories - Optimal reorder points for each warehouse location **Result:** 18% reduction in carrying costs while maintaining 98% in-stock rates ## **Start Asking Smarter Questions Today** Whether you need: - **Descriptive Analytics** (What happened?) - **Diagnostic Analytics** (Why did it happen?) - **Predictive Analytics** (What will happen?) - **Prescriptive Analytics** (How can we make it happen?) Our [**data analytics services** provide the tools](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/) and expertise to: 🔹 Frame the right business questions 🔹 Implement robust [data collection 🔹 Apply advanced analytical](https://analyticsn.com/?p=1512) techniques 🔹 Deliver executable recommendations **Ready to turn your questions into competitive advantages?** 📊 Contact our analytics team today for a free consultation ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Academic Research, Blog, Data Analysis **Tags:** Action-oriented, Business, Business Strategy, Communication Skills, data analysis, Data-driven, Decisions, Education Data, Effective Questioning, Innovation, Measurable, Note-taking Strategies, Questioning Techniques, Questions, Relevant, Retail Data, Small Business, SMART, Specific, Time-bound --- ### [How to Choose the Right Descriptive Analysis in SPSS](https://analyticsn.com/how-to-choose-the-right-descriptive-analysis-in-spss/) **Published:** February 20, 2024 **Author:** AnalyticsN **Excerpt:** Discover how to select the appropriate descriptive analysis techniques in SPSS. This guide helps you understand different methods to summarize and interpret your data effectively. **Content:** ![Analyticsn Discover how to select the appropriate descriptive analysis techniques in SPSS. This guide helps you understand different methods to summarize and interpret your data effectively. Discover how to select the appropriate descriptive analysis techniques in SPSS. This guide helps you understand different methods to summarize and interpret your data effectively. turned on monitoring screen](https://analyticsn.com/wp-content/uploads/2024/02/turned-on-monitoring-screen-scaled.jpg "turned on monitoring screen - Analyticsn") When it comes to analyzing data in SPSS (Statistical Package for the Social Sciences), descriptive analysis plays a crucial role in providing a summary of the main characteristics of a dataset. It helps researchers gain insights into the distribution, central tendency, and variability of their data. However, with a wide range of descriptive analysis techniques available in SPSS, it can be overwhelming to choose the right one for your specific research question or data type. In this article, we will guide you through the process of selecting the appropriate descriptive analysis in SPSS, considering factors such as the nature of your data and the research objectives. ## Understanding Your Data Before diving into the various descriptive analysis techniques in SPSS, it is essential to have a clear understanding of your data. Ask yourself the following questions: - What is the level of measurement for each variable in your dataset? Are they nominal, ordinal, interval, or ratio? - What are the specific characteristics or patterns you want to explore in your data? - Do you have any outliers or missing values that need to be addressed? By answering these questions, you will be able to narrow down the options and select the most appropriate descriptive analysis technique in SPSS. ## Common Descriptive Analysis Techniques in SPSS 1\. Measures of Central Tendency: Measures of [central tendency,](https://analyticsn.com/calculating-central-tendency-in-spss-statistics-stepwise-explanation/) such as mean, median, and mode, provide information about the typical or average value of a variable. They are suitable for variables measured at the interval or ratio level. Use the mean when the data is normally distributed and the median when the data is skewed or contains outliers. 2\. Measures of Dispersion: Measures of dispersion, such as range, variance, and standard deviation, help assess the spread or variability of a variable. They provide insights into how much the values deviate from the [central tendency](https://analyticsn.com/calculating-central-tendency-in-spss-statistics-stepwise-explanation/). These measures are useful for interval or ratio level variables. 3\. Frequency Distribution: A frequency distribution displays the number or percentage of cases falling into different categories or intervals of a categorical or ordinal variable. It helps identify the distribution pattern and the most common values in the dataset. 4\. Cross-tabulation: Cross-tabulation, also known as a contingency table, is used to examine the relationship between two categorical variables. It provides a tabular summary of the joint distribution of the variables, allowing researchers to identify patterns or associations. 5\. Descriptive Statistics by Group: If you have a categorical variable that divides your data into groups, you can use descriptive statistics by group to compare the characteristics of each group separately. This technique is useful for analyzing the differences or similarities between groups. ## Choosing the Right Descriptive Analysis Technique Now that you are familiar with some common descriptive analysis techniques in SPSS, let’s discuss how to choose the right one for your research: 1. Identify the level of measurement for your variables: Determine whether your variables are nominal, ordinal, interval, or ratio. 2. Consider the research question: Think about the specific characteristics or patterns you want to explore in your data. Are you interested in the central tendency, dispersion, or relationship between variables? 3. Review the assumptions: Some descriptive analysis techniques have specific assumptions, such as normality for measures of central tendency. Make sure your data meets these assumptions before applying the technique. 4. Consult statistical resources: If you are unsure which descriptive analysis technique is most appropriate for your data, consult statistical textbooks, online resources, or seek guidance from a statistician. By following these steps, you will be able to select the right descriptive analysis technique in SPSS that aligns with your research objectives and the characteristics of your data. ## Conclusion Descriptive analysis is a fundamental step in data analysis, providing researchers with valuable insights into their datasets. By understanding the nature of your data, considering the research objectives, and reviewing the available descriptive analysis techniques in SPSS, you can make an informed decision and choose the most suitable technique for your analysis. Remember, there is no one-size-fits-all approach when it comes to descriptive analysis in SPSS. It is essential to tailor your choice to your specific [research question and data](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) characteristics to ensure accurate and meaningful results. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Blog, Data Analysis, SPSS Statistics **Tags:** appropriate techniques, choosing SPSS analysis, data analysis, descriptive analysis, Descriptive Analysis in SPSS, interpret data, SPSS, SPSS data interpretation, SPSS guide, SPSS techniques, step-by-step guide, summarize data, summarize data in SPSS --- ### [2. Select the Right Data for Exploration](https://analyticsn.com/2-select-the-right-data-for-exploration/) **Published:** February 13, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn As a data analyst, you must decide what data to collect and use for every project. With a nearly endless amount of data out there, this can be quite a bit of a data dilemma, but there's good news. In this lecture, you'll learn which factors to consider when collecting data. Usually, you'll have a As a data analyst, you must decide what data to collect and use for every project. With a nearly endless amount of data out there, this can be quite a bit of a data dilemma, but there's good news. In this lecture, you'll learn which factors to consider when collecting data. Usually, you'll have a Data collection, Data sources, First-party data, Second-party data, Third-party data, Data accuracy, Data reliability, Data trustworthiness, Observations in data collection, Traffic pattern analysis, Traffic data, Data sample size, Population in data analytics, Random sample, Strategic data collection, Time series data, Business problem-solving with data, Data type selection, Historical data, Data timeframe, Trends over time, Data approval, Data bias, Data credibility, Analyzing datasets, High-volume traffic times, Data-driven decision making](https://analyticsn.com/wp-content/uploads/2025/02/2.-Select-the-Right-Data-1024x1024.png "2. Select the Right Data - Analyticsn")As a data analyst, you must decide what data to collect and use for every project. With a nearly endless amount of data out there, this can be quite a bit of a data dilemma, but there’s good news. In this lecture, you’ll learn which factors to consider when collecting data. Usually, you’ll have a head start in figuring out the right data for the job because the data you need will be given to you, or your business task or problem will narrow your choices. Following are some data-collection considerations to keep in mind for your analysis: ## How the data will be collected Let’s start with a question like, what’s causing increased rush hour traffic in your city? First, you need to know how the data will be collected. You might use observations of traffic patterns to count the number of cars on city streets within a particular time frame. You notice that cars are getting backed up on a specific street. That brings us to data sources. In our traffic example, your observations would be first-party data. This is data collected by an individual or group using their own resources. Collecting first-party data is typically the preferred method because you know exactly where it originated. You might also have second-party [data, which is data collected](https://analyticsn.com/1-data-collection-in-todays-world/) by a group directly from its audience and then sold. Decide if you will collect the data using your own resources or receive (and possibly purchase it) from another party. Data that you collect yourself is called first-party data. ## Data sources In our example, if you can’t collect your own data, you might buy it from an organization that’s led traffic pattern studies in your city. This data didn’t start with you, but it’s still reliable because it came from a source with experience in traffic analysis. The same can’t always be said about third-party data or data collected from outside sources who did not collect it directly. This data might have come from several different sources before you investigated it. It might not be as reliable, but that doesn’t mean it can’t be useful. You’ll want to make sure you check it for accuracy, bias, and credibility. No matter the data you use, it must be inspected for accuracy and trustworthiness. We’ll learn more about that process later. For now, remember that the data you choose should apply to your needs, and it must be approved for use. As a [data analyst](https://analyticsn.com/topic-1-data-analysts-need-ai-for-data-analytics/), it’s your job to decide what data to use, and that means choosing the data that can help you find answers and solve problems without getting distracted by other data. In our traffic example, financial data probably wouldn’t be that helpful, but existing data about high-volume traffic times would be. If you don’t collect the data using your resources, you might get data from second-party or third-party providers. **Second-party data** is collected directly by another group and then sold. **Third-party data** is sold by a provider that didn’t collect it. Third-party data might come from many different sources. ## Solving your business problem Datasets can show a lot of interesting information. But be sure to choose [data that can help solve your problem](https://analyticsn.com/six-common-problem-types-in-data-analysis/) question. For example, if you are analyzing trends over time, make sure you use time series data — in other words, data that includes dates. ## How much data to collect If you are collecting your own data, make reasonable decisions about sample size. A random sample from existing data might be fine for some projects. Other projects might need more strategic data collection to focus on certain criteria. Each project has its own needs. In [data analytics,](https://analyticsn.com/how-data-empowers-decision-in-data-analytics/) a population refers to all possible data values in a certain data set. If you’re analyzing data about car traffic in a city, your population would be all the cars in that area. However, collecting data from the entire population can be challenging. That’s why a sample can be useful. A sample is a part of a population representative of the population. You might collect a sample of data about one spot in the city and analyze the traffic there, or you might pull a random sample from all existing data on the population. How you choose your sample will depend on your project. As you collect data, you’ll also want to make sure you select the right data type. An appropriate [data type](https://analyticsn.com/6-know-the-data-type-youre-working-with/) for traffic data could be the dates of traffic records stored in a date format. The dates could help you figure out what days of the week there is likely to be a high volume of traffic in the future. We’ll explore this topic in more detail soon. ## Time frame If you are collecting your own data, decide how long you will need to collect it, especially if you are tracking trends over a long period of time. If you need an immediate answer, you might not have time to collect new data. In this case, you would need to use existing historical data. Finally, you need to determine the time frame for data collection. In our example, if you needed an answer immediately, you’d have to use existing historical data. But let’s say you needed to track traffic patterns over a long period of time. That might affect the other [decisions you make during data](https://analyticsn.com/how-data-empowers-decision-in-data-analytics/) collection. Now you know more about the different [data collection considerations you’ll use as a data analyst](https://analyticsn.com/?p=1512). Because of that, you’ll be able to find the right data when you start collecting it yourself. There’s still more to learn about data collection, so stay tuned. Use the flowchart below if data collection relies heavily on how much time you have: ![Analyticsn As a data analyst, you must decide what data to collect and use for every project. With a nearly endless amount of data out there, this can be quite a bit of a data dilemma, but there's good news. In this lecture, you'll learn which factors to consider when collecting data. Usually, you'll have a As a data analyst, you must decide what data to collect and use for every project. With a nearly endless amount of data out there, this can be quite a bit of a data dilemma, but there's good news. In this lecture, you'll learn which factors to consider when collecting data. Usually, you'll have a](https://analyticsn.com/wp-content/uploads/2025/02/image.png "image - Analyticsn") ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques **Tags:** Analyzing datasets, Business problem-solving with data, Data accuracy, Data approval, Data bias, Data collection, Data credibility, Data reliability, Data sample size, Data sources, Data timeframe, Data trustworthiness, Data type selection, Data-Driven Decision Making, First-party data, High-volume traffic times, historical data, Observations in data collection, Population in data analytics, Random sample, Second-party data, Strategic data collection, Third-party data, Time series data, Traffic data, Traffic pattern analysis, Trends over time --- ### [8. Data Table Components: A Quick Overview](https://analyticsn.com/8-data-table-components-a-quick-overview/) **Published:** March 6, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn Here's a riddle for you. What do a music playlist, a calendar agenda, and an email inbox have in common? I'll give you a hint. It's not a weekly jam session. The answer is they're all arranged on tables. Go ahead and check out your email inbox or a favorite playlist or look at your Here's a riddle for you. What do a music playlist, a calendar agenda, and an email inbox have in common? I'll give you a hint. It's not a weekly jam session. The answer is they're all arranged on tables. Go ahead and check out your email inbox or a favorite playlist or look at your Data table, Tabular data, Records and fields, Rows and columns, Structured databases, Data types, Boolean data, Spreadsheet data, Data table structure, Data analysis](https://analyticsn.com/wp-content/uploads/2025/02/8.-Data-Table-Components-1024x1024.png "8. Data Table Components - Analyticsn")Here’s a riddle for you. What do a music playlist, a calendar agenda, and an email inbox have in common? I’ll give you a hint. It’s not a weekly jam session. The answer is they’re all arranged on tables. Go ahead and check out your email inbox or a favorite playlist or look at your calendar agenda. There are tables in every one! ***A data table, or tabular data, has a very simple structure. It’s arranged in rows and columns.* You can call the rows “records” and the columns “fields.” They basically mean the same thing, but records and fields can be used for any kind of data table, while rows and columns are usually reserved for spreadsheets. When talking about structured databases, People in [data analytics](https://analyticsn.com/?p=1512) usually use “records” and “fields.” Sometimes, a field can also refer to a single piece of data, like the value in a cell. In any case, you’ll hear both versions of these terms used throughout this program and your job. Let’s go back to our playlist example. We’ll use the new terms we just introduced. So each song is a record. Each record has the same fields as the others in the same order. In other words, the playlist has the same information about each song. Like the title and the artist, each song characteristic is a field. Each separate field has the same [data type, but different fields can have different types](https://analyticsn.com/six-common-problem-types-in-data-analysis/). Let me show you what I mean. For the song list, the song titles are a text or string type, while he song’s length could be a number type if you’re using it for calculations. Or it could be a date and time type. The column for favorites is Boolean since it has two possible values: favorite or not favorite. We can view spreadsheets in the same way. The records in a spreadsheet might be about all sorts of things: clients, products, invoices, or anything else. Each record has several fields, which reveal more details about the clients, products, or invoices. The value in every cell contains a specific piece of data, like the address of a client or the dollar amount of an invoice. As a data analyst, lots of data will come your way, and records, fields, and values in data tables will help you navigate analysis. Understanding the structures of the tables you’re working with is a part of that. Hopefully, while you’re working hard on your analysis and those tables, you can have a little fun with a different data table: the one with your favorite playlist! ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques **Tags:** Boolean data, data analysis, Data table, Data table structure, Data types, Records and fields, Rows and columns, Spreadsheet data, Structured databases, Tabular data --- ### [Fundamentals of Structure Equation Modeling](https://analyticsn.com/fundamentals-of-structure-equation-modeling/) **Published:** June 24, 2024 **Author:** AnalyticsN **Excerpt:** Structural Equation Modeling (SEM) is a statistical technique that combines factor analysis and multiple regression. Key concepts include latent and observed variables. SEM involves steps like model specification, estimation, and testing, offering advantages such as handling complex relationships and modeling flexibility. **Content:** ![Analyticsn Structural Equation Modeling (SEM) is a statistical technique that combines factor analysis and multiple regression. Key concepts include latent and observed variables. SEM involves steps like model specification, estimation, and testing, offering advantages such as handling complex relationships and modeling flexibility. Structural Equation Modeling (SEM) is a statistical technique that combines factor analysis and multiple regression. Key concepts include latent and observed variables. SEM involves steps like model specification, estimation, and testing, offering advantages such as handling complex relationships and modeling flexibility.](https://analyticsn.com/wp-content/uploads/2024/06/Fundamentals-of-Structure-Equation-modeling-1-1024x576.png "Fundamentals of Structure Equation modeling (1) - Analyticsn")## **Introduction** Structural Equation Modeling (SEM) is a powerful statistical technique that combines elements of factor analysis and multiple regression. It allows researchers to examine complex relationships between observed and latent variables. SEM is widely used in social sciences, behavioral sciences, and other fields to test theoretical models. ## **What is SEM?** SEM is a comprehensive statistical approach used to [model relationships among multiple](https://analyticsn.com/multiple-regression-analysis-a-powerful-tool-for-predictive-modeling/) variables. It enables the testing of hypotheses about the causal relationships between variables. SEM consists of two main components: - **Measurement Model**: This part of the model specifies how latent variables (unobserved constructs) are measured by observed variables. - **Structural Model**: This part of the model specifies the relationships between latent variables. ## **Key Concepts** 1. **Latent Variables**: These are variables that are not directly observed but are inferred from other variables that are observed (indicators). 2. **Observed Variables**: These are variables that can be directly measured. 3. **Path Diagrams**: Visual representations of the SEM, where variables are represented as nodes and relationships as arrows. 4. **Model Fit**: This indicates how well the proposed model fits the observed data. Common fit indices include Chi-square, RMSEA, CFI, and TLI. ## **Steps in SEM** 1. **Specify the Model**: Develop a theoretical model based on literature and theory. Define the relationships between variables. 2. **Identify the Model**: Ensure that the model is identified, meaning there are enough data points to estimate the model parameters. 3. **Estimate the Model**: Use statistical software to estimate the parameters of the model. 4. **Assess Model Fit**: Evaluate the fit of the model using various fit indices. 5. **Modify the Model**: If necessary, modify the model to improve fit based on theoretical justification. ## **Advantages of SEM** - **Flexibility**: SEM can handle complex models with multiple dependent variables and mediating variables. - **Comprehensive Analysis**: It provides a comprehensive analysis of the relationships between variables. - **Theory Testing**: SEM is particularly useful for testing theoretical models and hypotheses. ## **Conclusion** Structural Equation Modeling is a versatile and [powerful tool](https://analyticsn.com/multiple-regression-analysis-a-powerful-tool-for-predictive-modeling/) for researchers. It allows for the examination of complex relationships and provides a comprehensive framework for testing theoretical models. By understanding the fundamentals of SEM, researchers can better design their studies and interpret their findings. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Blog, Structure Equation Modeling **Tags:** complex relationships, estimation, factor analysis, latent variables, model specification, modeling flexibility, multiple regression, observed variables, SEM, statistical technique, Structural Equation Modeling, testing --- ### [10. A Secret to Data Transformation](https://analyticsn.com/10-a-secret-to-data-transformation/) **Published:** March 13, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn What is data transformation In this reading, you will explore how data is transformed and the differences between wide and long data. Data transformation is the process of changing the data’s format, structure, or values. As a data analyst, there is a good chance you will need to transform data at some point to make What is data transformation In this reading, you will explore how data is transformed and the differences between wide and long data. Data transformation is the process of changing the data’s format, structure, or values. As a data analyst, there is a good chance you will need to transform data at some point to make Data transformation, Data formatting, Data structure, Wide and long data, Data organization, Data compatibility, Data merging, Data migration, Data enhancement, Data comparison.](https://analyticsn.com/wp-content/uploads/2025/02/10.-Transforming-Data-1024x1024.png "10. Transforming Data - Analyticsn")# What is data transformation In this reading, you will explore how data is transformed and the differences between wide and long data. **Data transformation** is the process of changing the data’s format, structure, or values. As a [data analyst](https://analyticsn.com/?p=1512), there is a good chance you will need to transform data at some point to make it easier for you to analyze it. Data transformation usually involves: Adding, copying, or replicating data Deleting fields or records Standardizing the names of variables Renaming, moving, or combining columns in a database Joining one set of data with another Saving a file in a different format. For example, saving a spreadsheet as a comma separated values (.csv) file. ## Why transform data? Goals for data transformation might be: Data **organization**: better organized data is easier to use Data **compatibility**: different applications or systems can then use the same data Data **migration**: [data with matching formats](https://analyticsn.com/3-data-formats-in-practice-know-data-types/) can be moved from one system to another Data **merging**: data with the same organization can be merged together Data **enhancement**: data can be displayed with more detailed fields Data **comparison**: apples-to-apples comparisons of the data can then be made ## Data transformation example: data merging Mario is a plumber who owns a plumbing company. After years in the business, he buys another plumbing company. Mario wants to merge the customer information from his newly acquired company with his own, but the other company uses a different database. So, Mario needs to make the data compatible. To do this, he has to transform the [format of the acquired company’s data](https://analyticsn.com/3-data-formats-in-practice-know-data-types/). Then, he must remove duplicate rows for customers they had in common. When the data is compatible and together, Mario’s plumbing company will have a complete and merged customer database. ## Data transformation example: data organization (long to wide) To make it easier to create charts, you may also need to transform long [data to wide](https://analyticsn.com/10-meet-wide-and-long-data-step-by-step/) data. Consider the following example of transforming stock prices (collected as long data) to wide data. **Long data** is where **each row contains a single data point** for a particular item. In the long data example below, individual stock prices (data points) have been collected for Apple (AAPL), Amazon (AMZN), and Google (GOOGL) (particular items) on the given dates. **Long data example: Stock prices** ![Analyticsn What is data transformation In this reading, you will explore how data is transformed and the differences between wide and long data. Data transformation is the process of changing the data’s format, structure, or values. As a data analyst, there is a good chance you will need to transform data at some point to make What is data transformation In this reading, you will explore how data is transformed and the differences between wide and long data. Data transformation is the process of changing the data’s format, structure, or values. As a data analyst, there is a good chance you will need to transform data at some point to make Data transformation, Data formatting, Data structure, Wide and long data, Data organization, Data compatibility, Data merging, Data migration, Data enhancement, Data comparison.](https://analyticsn.com/wp-content/uploads/2025/02/image-8.png "image - Analyticsn")**Wide data** is data where **each row contains multiple data points** for the particular items identified in the columns. **Wide data example: Stock prices** ![Analyticsn What is data transformation In this reading, you will explore how data is transformed and the differences between wide and long data. Data transformation is the process of changing the data’s format, structure, or values. As a data analyst, there is a good chance you will need to transform data at some point to make What is data transformation In this reading, you will explore how data is transformed and the differences between wide and long data. Data transformation is the process of changing the data’s format, structure, or values. As a data analyst, there is a good chance you will need to transform data at some point to make Data transformation, Data formatting, Data structure, Wide and long data, Data organization, Data compatibility, Data merging, Data migration, Data enhancement, Data comparison.](https://analyticsn.com/wp-content/uploads/2025/02/image-9.png "image - Analyticsn")With [data transformed to wide](https://analyticsn.com/10-meet-wide-and-long-data-step-by-step/) data, you can create a chart comparing how each company’s stock changed over the same period of time. You might notice that all the data included in the long format is also in the wide format. But wide [data is easier to read and understand](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/). That is why [data analysts](https://analyticsn.com/topic-1-data-analysts-need-ai-for-data-analytics/) typically transform long data to wide data more often than they transform wide data to long data. The following table summarizes when each format is preferred: **Wide data is preferred when **Long data is preferred when Creating tables and charts with a few variables about each subjectStoring a lot of variables about each subject. For example, 60 years’ worth of interest rates for each bankComparing straightforward line graphsPerforming advanced [statistical analysis](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) or graphing ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Data Collection, Formats, and Modelling Techniques **Tags:** Data comparison, Data compatibility, Data enhancement, Data formatting, Data merging, Data migration, Data Organization, Data structure, Data transformation, Wide and long data --- ### [Six Common Problem Types in Data Analysis](https://analyticsn.com/six-common-problem-types-in-data-analysis/) **Published:** August 8, 2024 **Author:** AnalyticsN **Excerpt:** In data analysis, different problem-solving approaches meet various business needs. Predicting the best ad methods uses past data to forecast optimal placements. Categorizing customer service calls can enhance satisfaction by identifying effective actions. Spotting anomalies in health data, identifying themes in user interactions, discovering connections in logistics, and finding patterns in maintenance data are crucial for improving operations. Developing these skills enhances problem-solving and stakeholder satisfaction. **Content:** ![Analyticsn In data analysis, different problem-solving approaches meet various business needs. Predicting the best ad methods uses past data to forecast optimal placements. Categorizing customer service calls can enhance satisfaction by identifying effective actions. Spotting anomalies in health data, identifying themes in user interactions, discovering connections in logistics, and finding patterns in maintenance data are crucial for improving operations. Developing these skills enhances problem-solving and stakeholder satisfaction. In data analysis, different problem-solving approaches meet various business needs. Predicting the best ad methods uses past data to forecast optimal placements. Categorizing customer service calls can enhance satisfaction by identifying effective actions. Spotting anomalies in health data, identifying themes in user interactions, discovering connections in logistics, and finding patterns in maintenance data are crucial for improving operations. Developing these skills enhances problem-solving and stakeholder satisfaction. Data Analysis, Problem-Solving, Predictive Analytics, Customer Satisfaction, Anomaly Detection, User Experience (UX), Logistics Optimization, Maintenance Patterns, Business Intelligence, Stakeholder Satisfaction, problem types, data analysis](https://analyticsn.com/wp-content/uploads/2024/08/Lec2.-Six-common-Problem-Types-in-Research.png "Lec2. Six common Problem Types in Research - Analyticsn")Data analytics is so much more than just plugging information into a platform to find insights. It is about solving problems. To get to the root of these problems and find practical solutions, there are lots of opportunities for creative thinking. No matter the problem, the first and most important step is understanding it. From there, it is good to take a problem-solver approach to your analysis to help you decide what information needs to be included, how you can [transform the data, and how the data](https://analyticsn.com/10-a-secret-to-data-transformation/) will be used. Problem types in Research are six typically. ## Six Problem Types Data analysts typically work with six problem types: ![Analyticsn In data analysis, different problem-solving approaches meet various business needs. Predicting the best ad methods uses past data to forecast optimal placements. Categorizing customer service calls can enhance satisfaction by identifying effective actions. Spotting anomalies in health data, identifying themes in user interactions, discovering connections in logistics, and finding patterns in maintenance data are crucial for improving operations. Developing these skills enhances problem-solving and stakeholder satisfaction. In data analysis, different problem-solving approaches meet various business needs. Predicting the best ad methods uses past data to forecast optimal placements. Categorizing customer service calls can enhance satisfaction by identifying effective actions. Spotting anomalies in health data, identifying themes in user interactions, discovering connections in logistics, and finding patterns in maintenance data are crucial for improving operations. Developing these skills enhances problem-solving and stakeholder satisfaction.](https://analyticsn.com/wp-content/uploads/2024/08/image-6-1024x535.png "image-6 - Analyticsn")1\. Making predictions 2\. Categorizing things 3\. Spotting something unusual 4\. Identifying themes 5\. Discovering connections 6\. Finding patterns ## 1. Making predictions A company that wants to know the best advertising method to bring in new customers is an example of a problem requiring analysts to make predictions. Analysts with data on location, type of media, and number of new customers acquired as a result of past ads can’t guarantee future results, but they can help predict the best placement of advertising to reach the target audience. ## 2. Categorizing things An example of a problem requiring analysts to categorize things is a company’s goal to improve customer satisfaction. Analysts might classify customer service calls based on certain keywords or scores. This could help identify top-performing customer service representatives or help correlate certain actions taken with higher customer satisfaction scores. ## 3. Spotting something unusual A company that sells smart watches that help people monitor their health would be interested in designing their software to spot something unusual. Analysts who have analyzed aggregated health data can help product developers determine the right algorithms to spot and set off alarms when certain data doesn’t trend normally. ## 4. Identifying themes User experience (UX) designers might rely on [analysts to analyze user interaction data](https://analyticsn.com/?p=1512). Similar to problems that require analysts to categorize things, usability improvement projects might require analysts to identify themes to help prioritize the right product features for improvement. Themes are most often used to help [researchers explore certain aspects of data](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/). In a user study, user beliefs, practices, and needs are examples of themes. By now you might be wondering if there is a difference between categorizing things and identifying themes. The best way to think about it is: categorizing things involves assigning items to categories; identifying themes takes those categories a step further by grouping them into broader themes. ## 5. Discovering connections A third-party logistics company working with another company to get shipments delivered to customers on time is a problem requiring analysts to discover connections. By analyzing the wait times at shipping hubs, analysts can determine the appropriate schedule changes to increase the number of on-time deliveries. ## 6. Finding patterns Minimizing downtime caused by machine failure is an example of a problem requiring analysts to find patterns in data. For example, by analyzing maintenance data, they might discover that most failures happen if regular maintenance is delayed by more than a 15-day window. Key takeaway As you move through this program, you will develop a sharper eye for problems and you will practice thinking through the problem types when you begin your analysis. This method of problem solving will help you figure out solutions that meet the needs of all stakeholders. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Academic Research, Blog, Data Analysis **Tags:** Anomaly Detection, Business Intelligence, Customer Satisfaction, data analysis, Logistics Optimization, Maintenance Patterns, Predictive Analytics, problem types, Problem-Solving, Stakeholder Satisfaction, User Experience (UX) --- ### [Basics of Pearson Correlation](https://analyticsn.com/basics-of-pearson-correlation/) **Published:** February 6, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn Exploring Relationships Between Variables: From ANOVA to Correlation Statistical analysis provides various tools to examine relationships between different types of variables. Each tool is tailored to specific data types and relationships. Previously, we explored Analysis of Variance (ANOVA), which examines the relationship between a categorical explanatory variable and a quantitative response variable, and the Chi-Square Exploring Relationships Between Variables: From ANOVA to Correlation Statistical analysis provides various tools to examine relationships between different types of variables. Each tool is tailored to specific data types and relationships. Previously, we explored Analysis of Variance (ANOVA), which examines the relationship between a categorical explanatory variable and a quantitative response variable, and the Chi-Square Basics of Pearson correlation](https://analyticsn.com/wp-content/uploads/2025/01/Basics-of-Pearson-correlation.png "Basics of Pearson correlation - Analyticsn")## Exploring Relationships Between Variables: From ANOVA to Correlation Statistical analysis provides various tools to examine relationships between different types of variables. Each tool is tailored to specific data types and relationships. Previously, we explored **[Analysis of Variance (ANOVA)](https://analyticsn.com/tag/analysis-of-variance-anova/ "Analysis of Variance (ANOVA)")**, which examines the relationship between a **categorical explanatory variable** and a **quantitative response variable**, and the **[Chi-Square Test of Independence](https://analyticsn.com/tag/chi-square-test-of-independence/ "Chi-Square Test of Independence")**, which assesses relationships between two **categorical variables**. Now, we shift focus to analyzing relationships between two **quantitative variables** using the **Pearson Correlation**. --- ## Scatterplots: Visualizing Quantitative Relationships Before diving into correlation, scatterplots offer an intuitive way to visualize the relationship between two quantitative variables. In a scatterplot: - The **explanatory variable (X)** is plotted on the horizontal axis. - The **response variable (Y)** is plotted on the vertical axis. Each individual in the dataset is represented as a single point, determined by their xxx-value (explanatory variable) and yyy-value (response variable). Scatterplots help reveal the overall pattern of the relationship, which can be described in terms of **direction**, **form**, and **strength**. ![Analyticsn Exploring Relationships Between Variables: From ANOVA to Correlation Statistical analysis provides various tools to examine relationships between different types of variables. Each tool is tailored to specific data types and relationships. Previously, we explored Analysis of Variance (ANOVA), which examines the relationship between a categorical explanatory variable and a quantitative response variable, and the Chi-Square Exploring Relationships Between Variables: From ANOVA to Correlation Statistical analysis provides various tools to examine relationships between different types of variables. Each tool is tailored to specific data types and relationships. Previously, we explored Analysis of Variance (ANOVA), which examines the relationship between a categorical explanatory variable and a quantitative response variable, and the Chi-Square Basics of Pearson Correlation](https://analyticsn.com/wp-content/uploads/2025/01/image-7-1024x750.png "image - Analyticsn")--- ## Describing Scatterplots: Direction, Form, and Strength ### Direction The direction of a relationship indicates how changes in one variable correspond to changes in the other: - **Positive Direction**: An increase in one variable is associated with an increase in the other. - **Negative Direction**: An increase in one variable is associated with a decrease in the other. - **No Direction**: No clear relationship exists between the variables. ### Form The form describes the general shape of the scatterplot: - **Linear**: Points roughly follow a straight line. - **Curvilinear**: Points cluster around a curved line. Other forms may exist, but for Pearson Correlation, only **linear relationships** are considered. ### Strength Strength refers to how closely the data points follow the identified form: - **Strong Relationship**: Points are tightly clustered along the line. - **Weak Relationship**: Points are more scattered and deviate significantly from the line. While visual inspection provides an initial sense of strength, it is subjective and prone to error. A numerical measure is required for precise evaluation. ## Pearson Correlation: Measuring Linear Relationships The **Pearson Correlation Coefficient (r)** quantifies the strength and direction of a linear relationship between two quantitative variables. Key properties of r include: - **Range**: The value of r lies between −1-1−1 and +1+1+1. - **Positive r**: Indicates a positive relationship (as XXX increases, YYY increases). - **Negative r**: Indicates a negative relationship (as XXX increases, YYY decreases). - **Magnitude**: - Values near 000: Indicate a weak relationship. - Values near −1-1−1 or +1+1+1: Indicate a strong relationship. For example: - An r value of +0.8+0.8+0.8 suggests a strong positive linear relationship. - An r value of −0.3-0.3−0.3 suggests a weak negative linear relationship. --- ## Conclusion: From Visual Patterns to Numerical Precision Scatterplots provide a starting point for examining relationships between quantitative variables, offering visual insight into direction, form, and strength. However, the **Pearson Correlation Coefficient** offers a precise, numerical measure of the strength and direction of **linear relationships**. This combination of graphical and numerical tools equips [researchers with a comprehensive approach to analyzing quantitative data](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) relationships. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Hypothesis Testing with Variance and Correlation **Tags:** correlation coefficient, data analysis, explanatory variable, form, linear relationship, negative correlation, numerical precision, Pearson Correlation, positive correlation, quantitative variables, relationship direction, response variable, scatterplots, statistical analysis, strength, strength of relationship, visual patterns --- ### [What can a Dashboard Look like: Types of Dashboard](https://analyticsn.com/what-can-a-dashboard-look-like-types-of-dashboard/) **Published:** October 5, 2024 **Author:** AnalyticsN **Excerpt:** There are three dashboard types: Strategic for long-term goals, Operational for real-time performance tracking, and Analytical for data analysis and predictions, often used by data scientists. **Content:** ![Analyticsn There are three dashboard types: Strategic for long-term goals, Operational for real-time performance tracking, and Analytical for data analysis and predictions, often used by data scientists. There are three dashboard types: Strategic for long-term goals, Operational for real-time performance tracking, and Analytical for data analysis and predictions, often used by data scientists. What can a dashboard look like? Types of Dashboard](https://analyticsn.com/wp-content/uploads/2024/10/How-Can-a-DASHBOARD-Look-Like.pptx.jpg "How Can a DASHBOARD Look Like.pptx - Analyticsn")For a refresher, consider the different types of dashboards a business may use. Often, businesses will tailor a dashboard for a specific purpose. The three most common categories are: **Strategic**: focuses on long-term goals and strategies at the highest level of metrics **Operational:** short-term performance tracking and intermediate goals **Analytical:** consists of the datasets and the mathematics used in these sets ### Strategic dashboard ![Analyticsn There are three dashboard types: Strategic for long-term goals, Operational for real-time performance tracking, and Analytical for data analysis and predictions, often used by data scientists. There are three dashboard types: Strategic for long-term goals, Operational for real-time performance tracking, and Analytical for data analysis and predictions, often used by data scientists.](https://analyticsn.com/wp-content/uploads/2024/10/image.png "image - Analyticsn")A wide range of businesses use strategic dashboards when evaluating and aligning their strategic goals. These dashboards provide information over the longest time frame—from a single financial quarter to years. They typically contain information that is useful for enterprise-wide decision-making. Below is an example of a strategic dashboard which focuses on key performance indicators (KPIs) over a year. ### Operational dashboard ![Analyticsn There are three dashboard types: Strategic for long-term goals, Operational for real-time performance tracking, and Analytical for data analysis and predictions, often used by data scientists. There are three dashboard types: Strategic for long-term goals, Operational for real-time performance tracking, and Analytical for data analysis and predictions, often used by data scientists.](https://analyticsn.com/wp-content/uploads/2024/10/image-1.png "image - Analyticsn")Operational dashboards are, arguably, the most common type of dashboard. Because these dashboards contain information on a time scale of days, weeks, or months, they can provide performance insight almost in real-time. This allows businesses to track and maintain their immediate operational processes in light of their strategic goals. The operational dashboard below focuses on customer service. Resolutions are divided between first call resolution (61%) and unresolved calls (9%) ### Analytical dashboard ![Analyticsn There are three dashboard types: Strategic for long-term goals, Operational for real-time performance tracking, and Analytical for data analysis and predictions, often used by data scientists. There are three dashboard types: Strategic for long-term goals, Operational for real-time performance tracking, and Analytical for data analysis and predictions, often used by data scientists.](https://analyticsn.com/wp-content/uploads/2024/10/image-2.png "image - Analyticsn")Analytic dashboards contain a vast amount of [data used by data analysts](https://analyticsn.com/?p=1512). These dashboards contain the details involved in the usage, analysis, and predictions made by data scientists. Certainly the most technical category, analytic dashboards are usually created and maintained by data science teams and rarely shared with upper management as they can be very difficult to understand. The analytic dashboard below focuses on metrics for a company’s financial performance. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Blog, Visualization **Tags:** Analytical Dashboards, AnalyticsN, Business Intelligence, Business Strategy, Customer Service Tracking, Dashboard Types, Data analytics, Data Science, Enterprise Decision Making, Financial Metrics, KPIs, Operational Dashboards, Performance tracking, Real-Time Insights, Strategic Dashboards --- ### [What is a p-value in Statistics?](https://analyticsn.com/what-is-a-p-value-in-statistics/) **Published:** January 20, 2025 **Author:** AnalyticsN **Content:** ![Analyticsn The Role of p-Value in Inferential Tests Inferential tests play a crucial role in determining the likelihood of observed data under the assumption that the null hypothesis (H0H_0H0​) is true. This likelihood is expressed through the p-value, which quantifies how often we would expect the obtained results to occur purely by chance. In statistics, a The Role of p-Value in Inferential Tests Inferential tests play a crucial role in determining the likelihood of observed data under the assumption that the null hypothesis (H0H_0H0​) is true. This likelihood is expressed through the p-value, which quantifies how often we would expect the obtained results to occur purely by chance. In statistics, a What is p-value in Statistics](https://analyticsn.com/wp-content/uploads/2025/01/What-is-p-value-in-Statistics.png "What is p-value in Statistics - Analyticsn")## The Role of p-Value in Inferential Tests Inferential tests play a crucial role in determining the likelihood of observed data under the assumption that the null hypothesis (H0H\_0H0​) is true. This likelihood is expressed through the **p-value**, which quantifies how often we would expect the obtained results to occur purely by chance. In statistics, a result is deemed **statistically significant** when it is unlikely to have occurred by chance alone, based on a pre-established cutoff known as the **significance level**, denoted as α\\alphaα. The most commonly used significance level is 0.05 or 5%. This means that if the p-value is less than α\\alphaα, the data provides sufficient evidence to reject the null hypothesis and accept the alternative hypothesis (HaH\_aHa​). For example, a p-value less than 0.05 suggests that the likelihood of obtaining the observed results, assuming the [null hypothesis](https://analyticsn.com/tag/hypothesis-testing/ "hypothesis testing") is true, is less than 5%. This indicates that the findings are rare or surprising enough to warrant rejecting the null hypothesis. On the other hand, if the p-value is greater than α\\alphaα, the evidence is not strong enough to reject the null hypothesis, meaning we do not accept the alternative hypothesis. ## Interpreting the p-Value and Type I Error The p-value also represents the **Type I Error Rate**, which is the probability of incorrectly rejecting the null hypothesis when it is actually true. For instance, if the p-value is 0.05, it indicates a 5% chance of making a Type I Error. Lower p-values, such as 0.01, reflect even greater confidence in rejecting the null hypothesis, as the likelihood of error is reduced. Returning to the example of the relationship between depression and smoking among daily young adult smokers, the p-value was calculated as 0.17. Since this is greater than 0.05, the data does not provide enough evidence to reject the null hypothesis. This means we cannot conclude that there is an association between depression and the number of cigarettes smoked per day in this population. Instead, the null hypothesis is accepted, indicating no significant relationship between smoking and depression in this case. ## Modifying the Research Question: Broader Population and Revised Findings Changing the parameters of the research question can lead to different findings. In a revised scenario, the analysis expanded to include all young adults who smoked in the past year, not just daily smokers. This adjustment increased the sample size to 1,706 individuals. The results showed that young adults with depression smoked an average of 351.7 cigarettes per month with a standard deviation of 300, while those without depression smoked 313.5 cigarettes per month with a standard deviation of 268.2. The difference of 38.2 cigarettes per month, equivalent to nearly two packs, yielded a p-value of 0.0285, which is less than the significance level of 0.05. In this scenario, the p-value indicates that the probability of observing this difference by chance, assuming the null hypothesis is true, is less than 3%. Consequently, the null hypothesis is rejected, and it is concluded that young adults with depression smoke significantly more cigarettes per month than those without depression. This finding suggests a significant association between smoking and depression in this broader population. ## Confidence in Findings and Implications The lower p-value in the revised analysis provides a stronger level of certainty. With a p-value of 0.0285, the probability of making a Type I Error—wrongly rejecting the null hypothesis—is less than 3%. This means that if the study were repeated with similar sampling, the conclusion would be correct more than 97% of the time. Such a level of confidence aligns with scientific standards for declaring a significant association, reinforcing the conclusion that there is a meaningful relationship between smoking and depression among young adults who smoked in the past year. This example highlights the importance of defining research parameters carefully, as changes in [sample selection](https://analyticsn.com/tag/sample-and-population/ "sample and population") and variable scope can significantly influence findings and their interpretation. It also underscores the role of the p-value in guiding decisions about hypothesis testing and the confidence researchers can place in their conclusions. ![author avatar](https://secure.gravatar.com/avatar/d148789f72c77490bc133dc2d3a3f2e72e85a3e0908ddd08fdba3237e0270e41?s=300&d=mm&r=g) AnalyticsN [See Full Bio](https://analyticsn.com/author/nabadmin/) [ ](https://analyticsn.com/author/nabadmin/) [ ![social network icon](data:image/svg+xml;base64,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) ](https://www.linkedin.com/in/nabeela-sulaiman-a06257115/) **Categories:** Courses, Hypothesis Testing with Variance and Correlation **Tags:** Alternative hypothesis (Ha), Association, Cigarettes, Confidence level, Depression, Inferential tests, Null hypothesis (H0), p-value, Research findings, Sample size, Significance level (α), Smoking, Statistical significance, Type I Error, Young adults --- ## Pages ### [Home](https://analyticsn.com/) **Published:** March 21, 2024 **Author:** AnalyticsN **Content:** ## Unlock Insights with Expert Data Analysts Research Data Analysis Services using Excel, SPSS, AMOS, and Smart PLS for students and industries. Accurate results, detailed reports, and Survey Development Consultancy. [ Get Started ](https://web.whatsapp.com/send?phone=923281922884&text=Hello!%20I%27m%20interested%20in%20Data%20Analytics%20help.) ## Research Data Analysis Help for Advanced Statistical Tests We are a team together…. Over 10 years of experience, Analytics-n Statistics Consulting offers you the best guidance with 100% results satisfaction. **Expert Team:** Our team comprises seasoned analysts with over a decade of experience in statistical data analysis. **Customized Solutions:** We tailor our services to meet the specific needs of each client, ensuring personalized and relevant insights. **Fast Turnaround:** We understand the importance of deadlines and offer quick delivery without compromising quality. **Affordable Pricing:** We offer competitive pricing with packages suited for different needs and budgets. ![Analyticsn Enhance your research with expert data analysis using Excel, SPSS, AMOS, and Smart PLS. 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Our **Analytics help** team assists our clients in setting new standards of excellence. ![Analyticsn Enhance your research with expert data analysis using Excel, SPSS, AMOS, and Smart PLS. Accurate results and detailed reports tailored for academia and industry. Unlock Insights with Expert Data Analysts Research Data Analysis Services using Excel, SPSS, AMOS, and Smart PLS for students and industries. Accurate results, detailed reports, and Survey Development Consultancy. Get Started Research Data Analysis Help for Advanced Statistical Tests We are a team together…. Over 10 years of experience, Analytics-n Statistics Consulting offers you the Analyticsn Enhance your research with expert data analysis using Excel, SPSS, AMOS, and Smart PLS. Accurate results and detailed reports tailored for academia and industry. Unlock Insights with Expert Data Analysts Research Data Analysis Services using Excel, SPSS, AMOS, and Smart PLS for students and industries. 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) ## Robert Analyticsn-Tutor is helping us for the last 5 years and the quality has been superior and their client-service attitudes are tremendous.. 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54dIsf7Z1lVkuEg3yfNI3Qe9YRV5uR36woxivtal7SHi0a3+03XmH5tvlxHaZfYn0rpPgp8P2+MXxQtbUQrb2juC6q2Sq+gJpnxB07QdG0iKDSbh7piP3lw8ZzK3tnoK+h/+CZ/wmt9b8TrqUvPltySOnSscVXVOk5Ho5fg3VxEab26n6Hfsu/CLT/g58P7SO1tI4PlG4lRux9fU161o+n/ANoaj9oZmEUjDy0Hf3zWPoaRxT2se19jMFVccHj0rutEtliuGjSPazH5AOdor4itU5nc/ZMLQ5YcppeH4NmoLG6eepOAR0X612dlpNvHcI23dzjaByB3rA0eJUvFdhtWI4IBwWNdbo8a3FyDa/Kq9Wc9a56cm73NuVx1JYYJJp2jjify9pIzxtq/baG0n7xlWMYwCvOffPrWhaaUIk+Zgu7BYjvVyHRmZB5f+pxleea0M1MpWwDTbSqttGBnqaddPGiPKy4aIdO9WG01oxu+6/8AKq6SeezLncw4PHWgN9UV2sPNjeTcpib5gMdDXJ+KdOS3eWBoVlj+95gPP0rsYXaJFhkADPkbR3rKvofPkaNo/LVVIAb19a0UmlcHA8y1rQDE8kLRqyzDAz1wa8k8f/C+G0ZpPLkIx8o+9j8K+hNSsFvWUCRvORuSB+lcD8TDEy+XzlRtbHaq9pzPU53Q0sj5J+N/wYbX/DXlna83zMoCDlf9r3r8x/2pvgxN8NvFct1HHi3kYrKF+6r56j2Nfsx8Q7CO1th5e5pNgPHYdfzr4q/af+HNj4x1K7sb9VaDUFZVZcZU84PTqDzXr5binCVnsfK55laq0mre90OA/wCCNv8AwVG8U/sV/HSG3N495oWrrHbXltcPuR407DJ4IGcV/Tl8BPj1oPx8+Gem+I/D90t9p+oRCRXjIYKe4z6jpX8YGsaXdfDrxxPayF47jTbgru6HIPBr9c/+Dd//AIKcTfDf4n/8K2128VPDniKQNZtLJhbW5b69AxGPqa+nckvRn5v7Nzg19pH9AxG8ZqN480addLqFpHNGQVkAOc1M8f8A+qtDjKckWKheOrrx1FJFigCjJHUMkNXXjqJ46AKDx1BLFmr8kWageOgChJHUMkdX5I81BJFigChJDVeSL2rQkjqGWOgDKiiqeOOnRxVNHFQARxZqdI6WOOpo46ACOOpo4qdHFxU6R0AJHFU0cdLHFmp44+KAEjixUyR0qR1NHHmgBqR1PHFilSLAqZI6AGpHUyR05I81MkdADUixUipTkSpEjoARUqK9h3gbunvVxI8Vk+PteXwp4R1LUpMeXY20lw2egCIWP8qNhxV3Y/md/wCDk34rXnxC/bt17T7e6+0Q6cyWscKPuEKxxgHntyTx2r84fDPha48QXrAN5MMY3PI3TA7D1NfWn7YXj618efGjxZ4q1SE3lxdXE+oXDO3ykyuSiKPxUH2Wvn/UfizNfTRw6fa2Vrbx4JUwjBbvXPTn7p7NXDxco8z+Rz3iG7hDw6bZ+cyqQPm7mvvX/gmF4LkWzS8uP9RuxHETjdjqa+P/AAR8MR4n8TfbrrbHC6zSFiNoJVc5/PP5V+iP7BOgx2eiRrGjeXawZDEYABGcmvLzSovZ8p9Lw/hn7Zyn8j698IW/9peJ4oWZT5ce/gfcrvtGhhtnchm+XhiR96uJ8ImOwvLOQf8AL4hjJ9Peu+0qzd7qFP4T19xXxsn0P1CG1zW0Hw6tztZpGEb/ACqGHeuksXXT4NsMYZoztx61VtrVreAKq7gBmtjQ9PW6tVZm8s5y208mqULK6Ji77nRaYVu7PzDG25l5X+7U1sJI0XapPseKfaReTtZdy7U61djtVWIszN7n0rSKbOCTszP1ZVMPPBb72DUUlhHDarsb5iODV2VLe7wqz5OfSrF7avBar5eyT1HetIx0D2lrI5G+lmS7V/vqP0qrqFsLj95HJ85559a6e/0rzrVjtxxk+1YrWjQ223Yq+WeTnqKmzO2M0zmbmykhgLTMpLHcdv3ia8/8X+GftkknzMGkBwW616Xehnc/u1Oc4PpXM69ZNImQ/KcjPap1Nbxa0PA/HGjT6bEy43bc/Ma+b/2gPCy3+gm6hVftli3mHC9Vzz+lfWXxgmaGzjaFFaTzv3jAche9fOPxGs1vrm5iZmAZGQ8dQa2pTaaPMxVO8Wj8vv2ztG+wfFOO72qrahbI7gdyMj+gqL9lvVJ7Lxpp8tpcLa3drdRsr7tpwWA4PqDg/hXR/twWEZ1PTbhVbfC0lu5PqDwK87+AVs9/46t4FbYJCAW/u+/54r7alLnoKR+Q4qKp5hKNtGz+rj/glR+1K37RnwLs47qZX1jSYkhvNzZaRhxu/EV9VmKvxv8A+CBPxLbQP2ktW0CS6zDrdoQke/5RIq+aML2ypP5V+ykYJQZralJtanlYyChVaRXePFQvHV546hePFbHKUpIs1A8dXnjqF4sigCk8dQyRZq7JHioXjoAoyR1DJFV6SPioJIsUAUJIqhkjq+8dQSRcUAZccXFTxx0scdTRx0AEcdTxxUscVTRxUACR1NHFTo4qmjSgAjjqZI6EjqaOLNABHHmpo46VI6lROaABI6lSOljixUyR/wD6qAERKlRKVEqVIqAGpHmpkSlVOKkVKAESPmvGf+ChHir/AIQz9jX4iXnmLCx0S4gDk/d8xChI9wGJr2l/kTPSviD/AIOCfjJB8Hv+CdHiCeSYxz6pdwWcKj70pLZK478KTSlsa0Veoj+aH9sTxNJf6x9n2tGiyliw4BXkJ/ImvJ/h3cWqeK7Wa+UyW0MokaMf8tDmuz/aL1GPVNbuLwzS+delJVt2XAji8v5W+vNeZaerecCpZSp4xURjaNjrxFR+3ue/eBpLjxJcJeXB8mzuHlEUZO1WTdubA/2iNtffn7JWpYtrGxhjCqx8y4A7rgYXPoBXwX+ztocusRWqyK0yQ5aMFciIEgf1r9IP2UvAEnhvwPHqjoskswPGcs2a+bzeVlY/RcipuTTR794Rm3oZCdzqfk9APavRvDBae4hD7lTrle9eZ+BLbb5++TDB8ouMAD0rv/DOrS2l0v8AHGvBJ5APtXy8os/QobWPS4h55j25wcAgdq2dOsPsczbQxVhzWF4Yv/t80YVVx34rsorMXiRqpI+XLFTXRHc5akuV8qJNNeSZY/lO3oc10TD7VF1B9axbLTTJc7csu08HPWuhs9IaGH7wbvzXZRp3TR52IkrmYdMEBZlVD7VHIuEz5bZz2PFalzC0cJ3BeD2rPkl5G0bcnnNTKPLoZxk5asr3EW/5NxXcpPtXO62skEbD75x97p+ldNczRySKzFhgY/OsnUmUrJghlUEY71nJ6nVSqNOzOLu4mt7eRlZmZPmP0rOlaO7tiSMBh071oX8klrJIG6n9RWRqMrtblSd3yk+mKxOq2lzyP4p3K2U9xDtWRWXHv6V88+PoBFFcSDiaMAknuDxX0Z40k+2rdSeWN0fyLlevNfOfxj3afY3kkjbGkj2rj2NVT1lY5amp+bf7cK77ksF27b9iR6ZH/wBavNP2cNKOr+P0jEnl7Y2cH+8RyBXpP7aksjIrvtPm3Qy3c43Vw/7OEn9mJqmolVH2aW3UOc/KGLZx+Ar7fC6YdH5Lm0b5o0j9C/8Aggx8Upbr/go54Ps5JJDIoeN1J3LhUeIn67SDX9JMIBHvjpX81P8Awbt/D5vHX/BWSIw4kttDtr+/kYDHyYVF/VxX9LEKbbjHUYrpp7Jnj5hZ1NBrR1E6VbeKonStjzym8dRPHVx4+KhkizQBTePNQyR4q46c1E8dAFJ46hkjq5JFioXSgCjJFULx1eePFQyRUAZMcdWI48URx4qeNOKACOOp4o6Io6sRx0AIkdTJHSolTRxUAEcVTLHQiVMkfNAAiVMkWKWNMVKiUACR1IiUsaZqaNMUAIkeKkVKciVIqUANVakVKcqVIqcUAQzqRCf0r8r/APg6E1CTWPgF8OPDcLMZr7xAbl19Y402t/6Hn8K/VaVPMi+X+Kvy3/4OMtJs7rQ/AuqzW8tzN4bMtxLErYBSR0QfjuB/Ksq01GF2d+W0ZVsRGEEfzsfFzSri/uo7ibiTy5DM3qgban/oI4rk7KzfTLKEmNVa8P7vP93HX9a+m/jX4P0u3+C32i1bdNMm4ExjzHJdjt+gLH9K8X+H3hq11Tx3a2lyWb+y7ZXdFHWQHlf1GfoazlU9256NTD/vkmtW7H0R+w98I7rxZ4rs4AzRwQwAMM8KTzz+GK/SLwRplvounx2ECr5KoEA6Lx1r5z/Y/wDBcXh/wob8xiGTUmBjZUxuT1xX0RpFtstLWNmVpluBzkcivj8fV556n6tk2CVOjc7jwnau3iJo4R5iK4MmegGK7yzsfsgkkjHy8EgjrXJeFYJdMuLhT+7ZjtUfxEep9K68avFD8qyKwjUDJPDnv+VcPs2en7W0uW52Xg9hNYyyJmM8YAGMY612fhZnjVpg+5Jn+UDsPSuC074r6TpstvYs0bTTqC2AMp9RXdaB4y02S1h23NvGu/5l3j5RWtOKW7OepWilqdTaReZMyry2O9atvayRR/dJOOu7pWPoOqw3ErCGWORvM2jY2celdHKfs9x5ciurHj6muynGyuedUrJysjMnRi/+zVHULRJXX94qtH82PWtOa5if7vy7M8epqG5sobpd+7DdQfUUSp31RcHbVlK4tluLT5fvdyK5/wAQ6YquwjCrlTznmuk3KtxDGmFXcT164rP8Szxpcu0m1tqgggd6ynTVjelK8rHntxpym4H7za3X5u/0rB8RQNFNIY0Zotp5B712U8K3L+YV2+WpjH0PNcrrNr5Ky7SVx1z0zXI4noRqX0PL/Gt7BZ6TNuUFnOF+vvXzX+0K0T+C5921bwsVUeua+kfiRtSymj+XLc52/d9fzr5V+PUDf2Z5bN5m0kBQefrmnSu5aHLibRg7H50fty/Lf2ao+URsEejY5rhfA96mi/B24kG7dd6gFkwcZVV4/Imuz/bImWfWRAsbLtuS25+nT1rgtetF8OeEtHsV2sdQzds3qOgXH4V9rg/4CR+S5hf69Ooftf8A8GifwZhv9T+KfxEmWOSTNvpFvIFJK5HmPk468LX7grBtkyPTr61+XP8Awal/C+48EfsEapqsq4j8UeIZLuEjkbFjVPwOQRX6mIcnb7V3RVkfP4iTdRkLJUbx5q0yVG6VRiU3TionTmrkiZqGRMUAVHizULpzVx0qKRM0AUmjqGSKrjx1C8fNAFJ46heOrskVQulAGTGmasRR0RR1NGlADo05qaNKbGlWI46ACOPvU6JSIlTIlAAiVPHHiiNMVKiUACJUyJmiOPNTIlABGmKkRKFSpVTNACKlSKnFKqZqVEwKAESOn7SRTlWnEYXNAEMpKQ/L/D2r81/+Cw3gzUviZrmv6FDbtczXdhALUMvyooBJOf8AfB+mK/SlQZTxwpFfKX/BS5LbRPDmn3McayajqUD2CHHJDOn8gW/OvPzJtUW10PouFaiWYQT6n8+f7UPwfm8FaTZaCGM6WWxpkQbWbOCW/wB1jxXzP8GvDtx/wt+GYYkkkujIzE5BjUnd+BORX6p/8FAf2cp7TwTqWrRqBdapaMVbGPI2fcXPZRgV8AfAXwIr+M7OHyWaa3kjglwvLH72PoT/ADrzsJiG6UubofY5tgILFRnDY+8vgjpC3fhe0kj/AHccUY8qLPKjH+RXqGnwLpyQKyrFvfIc9S3YV5z8N4G0BY45Wj3xoNxTgDuBXbC6n1vUrS33tHbh/MLjsw6Z9q+cqS5pn3dKPJh0/I6Txn8RYfC3hu6uY1a4vFiw2znkcYJrxHTfjZeeI9TnuLrUharBJ5byAHJHUIi9Nx6V9Bal8OP+E105ksrqSG8VcXFumP3g7sB3zXjPxN+D/hb4abLrXPEVjYQmXzX+0OIipHqPUeorSnFc3vbHh1qjnpTvc4/45+PvEngPwzDdaVbyS65rEwaGR5C0gzxyM4Ax+VeT6N+1B4msdXXT/wC3Z9RKruvnhuNyo+eUGOMg9+9Z/wC0B+2F8NRLdWg1jXtQtmJt5JbC3JXaOwcnv7da8j1T9obwJaeBlm8M6L4j0+2mdoUvntY5GeVepAB6gHvnk5r0aeCk1zJHj18xpwl7Nys+x99/B3/goFp/gK3jgu9V+yyqweT7TOc5A46cD6V9I/BP/gpbpPjvUYbW4vI5Fk4SeRtoc/7PXNfj94I8N6kbm3stNn1zTptWiE0i6hZmNZ0bnfuJPBzmvoPwP8FfHngMWOoXFnbzW8afuprTGMduRWNSSi+U7aFJyjzaH69R+PoNVsklsZoZFlOd2c/l61cl8YLFC0a9UGQT6V8kfs1+PdY8Q6bawzRzI1uNpLoVwx9K9sknvtM8+W6bKqgUEnqK5nVa2Pfo4VcvvHcWXiT7RqbSFsqi7cg8LnvTLm9OqSiHczbgTg9h9a8O1b43W/hHUZGuLqGGK4baFdscL1NeBftHft73nhCKabRdWtGSIHb5eSX9jjkVPtvsnLWfJJuKPtq81i2sgsc1xBD83O5wOemK5Dxnqqx3EipdW5XglPNHNfmJ4j/4KFal4q0GVNQ1qz026tXMkAlu28xuM/KQpyAexrzqX9uzxIdbhmi8YQ3e7DCfz9iD/ZdGHf1FW6NWXQ8z+0oQdrn6afEa3hu7dp45PlYfd3ccfyFfNHxtsFK3BVS27t6fSvET+3j4kvo7WOFVu7ieXa+DuhbHGD22npyetelv4vk+I1vZi5isdIu7iMN5Mt0MZ/ng1vhsFNzu0RXzSElY+Av2zYjaXBgK7WklJAY8nmvIr66m1yTw9ayjbJao1uQOo+ckfzr6E/4KNaJZ+FvEunQwzR6hNNIWlmjHyIRztX1Hqa8f8OfD+68SeO9KjViI7mRZ4JG6FN3U/ipr6uglGmkfnuZe/iZSj5H9Zv8AwSH+Bo+AX/BPf4X6GVZJ5NJS+nVhgiSb943/AKFX00kGHz7YryT9hDxHqHif9lXwTJq8K2uqWWmxWVyiLtUvEAgIHYMAD+NexhMOcfl6V09T52d+Zldk4qNkq06ZFRMmKokrOlRyJmrLJiomSgCq6YqF0q46VDJHigCpJHmoHSrjpUUiZoApulQSRcZq46VC6UAZcacVNGlNRamRKAHRR5qxGlNjSpo0oAciVMiYpI0xU6JQAIlTRpmkRKmRcUAKi1IqUIlSqlAAiZqVUzSIualRcUACpipFWhVxUirmgAVc04pvQilVc1IqUAQC3CwDFfH/AO3DfHxZ+0H4U8P7POgtYXuJkHQE/wCRX2Hfj9x+OeK+QPjssdl+13HNN964s2VGY8IdicV5mav9zbzPqOEYp42/VJ2PkX9v7Q28WeFLvQ7L93bSw7ZGIwwGegPvivgb9mX9n2bTPGl5qt9HNHG0khhR02q+G2q2T6gDFfpH+1LttvBetMoD3PnpHEScMM5H6E5r571fw3H/AMJHqTRw+db+H0hDwksE+7gnP/AmP4V42H+CVj77HStOmn0OZ1HS4LFsruyxUgqOvvXffBSeGXUvJurVLqNSAXdRzntiua1K4gikguN0aRscRYX5HUd/8K674Wz2uqXm9Y23KeQo/ImvDqRtK59VKtGdPlWx6dqvws0TxTF51m15pl8oxHcW1y0W32PXNedw/sUeE/FniaeTxhb3HiWaaQNFNqTmRYGHTA4XafpXuOh+E/tdjCqt5ES87s/M1dJF8OI9Q24aRYyOrNkn3ogry5k2cscOorQ+cfE//BL7wb4h8P39lZ+H7C1tLtlbbbRhSWB4YHFeY2v/AAR+0nwpd24hhvLWCebzJoJIh5LcnkYOAcDrjNfeWn+E9W8PCRbG/t5oyMBbhTx+XFGs23ivVkWP/iWqq8FhuOP0r26eNlCNup5dfK4VZczSsfLfxe+ANv400XTLOG1h0O90eNIbK6jhUqEX5djE9Q2OhrV8L/s/698P/CEt4UENvFKpFtjzbe4Q/wAa/wBxvVenpXr2ufDzWHvf9Mu7eRf+mS4AHXHNVhb6hpdh5M15LJbNINqM2UGK5a1eL1e51U8HPlUKa0Ob8MWb6N4ttdsaqswEjJjIye2K9Q8ePEnglnmHlqACMfwn6VwOhXa6v44ikcG3VTjg/eA6V2HxS1KK40KSENzJGQrfh0xXBSlKzcj3vq9uVHwX+0v4hvPGfxEh0DTpkQ3j+XJLIM/Z07sK8n+JH7MvgHw7YG23eKta1CYYIt2KgydOCcDGfTNfQfgOwsdI+PTalqELTLJK0BzHvMRZcKQPrXutr8CvB8WqQ+IpvtD6nLkLPMA67uoGT93HoOK7cLRjNaHzuOmo1pRbt20PxV8c/DjSn8f3Vi+m+LLO401Jnu2uhH+7SNctyxGSBj3Neifs+/BH4QfGbRXsdF+I2m6dr03yompwrHMxPHy7xj8jX6CftW/8E2/C/wAeH1LWBceILW/1BvOWazO6IFuGJCkH5sYIzXzPpP8AwS08LeF9K1axks57rULseVbXSW5ia1xzkZPU+te1zQULN6o+WlhcTUq3UVY8b8Zf8E5/FXwfW8uIfE1vfWspIaWBgcrnOT1/SuN8EeCfE8XiW1N1PM1xprlTcM7uWQdODXrGk+Bfiz+zRe3VvK194r8D2ZDTBzulgXOMqG7j0r0RfG/h/V2VrG3uXurxA7oIvmXj+IYwKyp1m3uOVFRT0eh8S/tqSXGpeN7GS785pDCUBz8wyMcjp+VdZ+xd8KLr4r/HjwP4Xt45Gutd1uz0q3AzwNyhsnt0P0zXQ/HjwCvij4uMzRrBHawrMpbhVYEYz69cfj7V98f8Gz37MWm/Ez9pbxF4m1Jbdrz4VNutbbaMyzXKbVlJ9Y8SfiR6V6tGWiifO4yEabnWR+5Pwu8AwfDvw7FptvuMasOo6YUKP/QRXUmPb0qWOP8Adj6UMuK77WPkW23dldlqNkzVhlxUbLmgCuyYqJ0xVl1zUTrigCsyVG61ZZKidKAKsiYqF0q265qF0oAqSJmoXSrjpUEiZoAyI1qxGmBTIlyanjFADo1qeJMmmIuasItADkFSon50iLUsaZoAfGmBUqDApEGTUiDAoAcq1Iq0ijAqWNKAHIvFSItIq5NSKu40AKi5p6rmkAzUiLQAqLTwMmgDmngbVoAhvUzBt9a+O/25tEurr4lxy2LLHf2tvDeQHpnBZWHuMV9iyozRDv8A1r5m/ba0F4vGvh3Uh+7ivIpbF39x8yj/ANC/KvNzaDlh3Y+m4RrKnmKb7M+Nvj1Z3XinwlqWtQs1rZ6efOd2+ZSU+8TXn9rCb6z1W8Lbv7QieW7JYbTugMkZA9Bgqa1f2iPH2oeEfhR4o0/To1vdkcjXKsfkWPd85PuQeBWD8KLO41KSHS9/mJ5aNMQgyIvLZQuD1xuP1zXlZWuakz67iGo41oOWx5j4vuri81PSlQLDutUGCn+rOBngd8cV6B4Fj+z+ILe3tJFht+GmbbtfaoyxOep56VnfFO2bRNVtZLa1hWYiXy4VwzfKcBj6Z680aRdtqklnOsSq0Un2WS4V/leRsfKo9u5NeViKfvtH0OCxHPRVj6W8Eait4sf75hboeQwxketen+Gzb3MX7rcrMeC3P0r588P6xHon2WGSeSSRRtAXnOPWvbvh1qccttbvvVdoBcN2zWdGylY9l0n7DnOg1bSLvcvl7VaP5mB6EVTura+89VVvLDdc9TXZRXVsx2yMHUgEZ7Vk+JPEdjpKmWRWaNWGWx2+td7ow7nn08RNacpxPimK6t7JuCy9WI7fWvL9Zubi+jlt3Zlh3Y6c4HSvVvFvjbTWtSkMisWydgP3gRxXz/47+ISwalayRFlguJzGTt4JzivNxEddD3MFU5o6qx23hCwX+2I4ggfIG1wOOfeuo8bWqro0gR2U26HcMAhjWT8NYjfTxypuVpQBjHyrWx8SUeLQ9v3myQeKqnD3bM0qSblofEerapJoXxH1KZseXGxwOeoPBr6Y8FSRePvBcME1w0bTIGHltxux6V83fG21hsvGTReZ812dufU17/8As5pGNJt7Ur5kkKbFbP3eK1jL2a0PNqYaFWV5bnVaJ4Z8SeArfZbXkOo2RbPlTfJIB6CuN+IV9Nfy7ZtJvjIz5PlgFT+or1ie/nsbdzj5Vwp3tnPvWLZ241W8kaaPnfnDDoP/AK9YzqSfU2p4OC3R85+O/Cepajp4so9Na0tJBuYS8tNjkjH6V5p8R9Hs44Lu8hsvs7tGscYSMKUx1zivrj4hLHNp7s1u6NFjHzY3Yr5y+M9tHJpM6x7VaZSTx/F2H41thbuSR87mlBJOSPkH4r+Bpb0Xn7h5ppZ4wuG+8uSPrySD/wABr9R/+DbH4KP4Mg+JHiSa38ubxItrKzE8kZcj9Oa+QtM+E7eN9MS3+wqsrb1R+dw3IQMc/wB7kV+un/BKX4GXXwc/ZZ02e+s1tL7WIopDHt+ZYkXamfryfoa+spU5KafQ/PMyrU1hZRv7zf4H1NGNq0jrUqrlKaRzXoHyRAy4pjripnWoyMUAQutRuvFTsu01Gy4NAFZlqNlqxIlRMMigCu4yKikTIqw65qNxg0AVHT86icVZkTFROtAGPGvFWI1qOMc1NGKAJIlqZFpiDip4xzQA5FqdBgVHGtToOaAHKvapUGTTEHFTKMCgB0YyamUYFMjWpUGTQA5RgVIBgU1Bk09Rk0AORcCpQMCmoO9PUZNACoMCnqu6kp6jAoAUDArzv9oz4W/8LS+G97ZQxk31v/pNk56rKmSOffp+NeiAZNR3wPlcAtgdBU1IKcXFmuHqypVVUg9Uz8Vf2gL2bwb8KdVs7qORfEGpXbwS27geY5B+4R7Vofs1yxeHPADavq0N0mpGZIlimYZlcocEgdFXivpz/gptp+jW9nHMmj2txr1zdriSOAb+vAz9OSa8L8EfC61l+Hlw0ksn2e3LOxc7maRgcYPfnpivPo4VUbpH1WMzR4202cT4v0CS/wDEu+OOa4aSGSS6lWP5IC/QZPOOK43xCZdF8NfaDPFHZ6W5OxFPJX7zgj045r2LT9Js00yOTULpYzdIskkJmHyAD+IdcD0715t4i063v541vIftWneeu6FFIEkXvj7uW7emK8fEUVzNn2WU1uWmkxfhb4lbUpVuPP2rI25QgLuR6sx6Z9K+iPh5qnnxRNvkXcNuGPAx0zXjfhTwfZ+HtJkur1YVvJXEqWtqh2RKc4XPr0roNO8UnTE3STNH5ygqmceSvpXmypu92fc0cVGpQ5Ee3XHxEjtxIPMkRhhTj7p/CuJ8bfGn7En2UN5kcuQAp5NeY+O/jNa2cUarIPMHCqpyzfWovhB4Zutd1walqPmTtI/+rLfKg61nKUpSsio0IKPNY14Nbm17W/ImWS36ES7sMQewFVvGnh6G3vNPhEwNtauLhw38THrVj9qTwX4oGjW2ueCl0+fUtKRvtFhKTuuo8AjHowwMV4p8Hf2wV+KOryaHrVjJofiS1f8Ae2twMeZ67c8/hTklz2ZVOPu+0Ptz4a6Fb/8ACMQNZsrt988c81n+PbNruSRI/MZ1GNpHAzxVX4RfEODTdEVWZOffhPQ4rS8XePNNl06Z1khV2+bzN3JxXX7NSXY82GJtUd3fzPj/APbB+D11ouu6Trkdwmbd/nt88tnoa9W/Z4ih1LQ0mWZZJIodpWJ8fN1yfw4rwL9pr433Xjf4maXo8U8ZtrC4BuJc8snOF9OM12n7M3jr+yzqlvZyK+64CSMo3LwOme1ZVabUdTSnUg56M+mLDWFcXEci7kfCZx9yqV5bNbzSTeYERnzuz0xVHRtaW7EkjKuGOMAY5xVfxDq7QWu2ZlfALMR8u1c964le2p2SktkZPjvxCJk8pvLaObkPn9a+bfiFcz6nrF5psqt5d0myCUHGx9+N34EYr0b4zeOf7N029S3jaRo1O3bztGOtfPFv48ufFd5HeSecosYI1lTP3NzE7h9DmvQy+m3O7Pk+IbwpXR9Mfsz6Dea/qml3EX2eeOPVY4wxHLAfwn/gS5Nfsd8P7hLzwbpsscaxxtbRlUAxtGBxX5R/sW+CDba1p2kq8TSq51TzAOobaAwx3x1r9afDFsLTRoYwqrheg6fhX2mnKj8ZxUryuXCMU1xkVKwyKZVHKRkZFROuamYYNMcUAQkZFRsMipWGDTHGDQBCwyKhkGDVhxg1FItAFdxg1Ey9qsMMioXHFAEDjIqB1q045qCRaAMeMZH1qeMZNQx9RViLn86AJkHNSIOKjjqZOcUATRjFSp92okGBUy8YoAkVe1SqMtTE61JHQBInSpUGFpg4FSUAPQYWnp92m1IvUUASAYFOQcU2pBwKAFUZNPpsdOoAcg4qDU5vItZGwx2qcbRk1ZHAqrqEpWzYr8zYPy560AfA/wC2lZf2x8S4vtMBuY7NHlWMtsZiwAOWBwMAHrXjFleLbpDo8MsflmIzHD9WPQKM5yvQZ969+/b38OzxeOLFY4/sq36P55DhVUAdWx25xXzXpfg+CB7e41JHu5reQCJ0k+VpDu2jI546fSs60banrYOV4oms/hYfFGgXV9JMtnMl6tkGkXdI0I+8c+9afhvwBnXNSWSBGjtjGRJIuY4kZeEA7txkk+tbGm+F18S+IdFmLTQWdnIVdFYtGjDGHx/Fk5x/umuxvtdDa/d6KLGSYL87kLjYe2cdz1Oe1eViKaSbPqsLim2oRPOvEfhGxt9Oa62hSx2DP3pHPt6cV4B8avEerSa3b6NpNuy3l6diu+cRDoScV9L3Whal4gkulvvs9ukQYqyjiFe2Pc9/pXkmuz6f4d8dyXmpSQtb2cOI40XM0oJ+Qgnplg34V43JzzUT7j6/9Uw3tFqzP+An7M95Z/8AE219W1CJptgml3KuewA75Pevofwd8OrK21qG2s/9TAnkyP6Sg5IP5jmvMbT9qnwlorBPEmtafo+m6TAVENxMqt5zJlQSGKnbHg4HevXPgx+0P4N8bTXk1j4i0VluLeBrV4ZU3FXUsXI+nBz3xXofVYbRPlcRxFjG7zujuJ/h9b3V1JJ5TO8KjKqMdfX8j+FfJP7c37FWn/Gyyt9c0ILpPifT28621KH920gU8K2PvdOpr680Hx9t8T6rCkyXVvfXBWzeLD5URqgOfQbmOexzTfGek6WukQ2yNFJdSW5hKZO1Echh+NTUwaqLY5cLxBXoz95uzPyZ0z9v3Xfg4smgeOtLvrfVrX9wbyKBjFeJyA+Bz26+1Y/xK/4KhaTofhdrHR4dR17Wp+IreOFkWPP95mH6AV+hHxA/Y88O/F3xbajU7KwunjibbIYwypGH3J0HXFZfi39g74d/2xY6ta+HdKXWLdMtL5IjVySAF6dcqQDRDBSiryWh2VOIoSfLHR9D8m/Afws/aF/aY8QXGpWNhP4bs45tzSzDyFBPQZb5jX3F+yv8JLj4BfDqLR9Snk1DVLyVri9u5c72mPXBPavrrSvCFnpem2VrfQxx2sh+zwyAcI5BYBv7wxxn2o1r4QQ6pcyLJbrHNHASgB3JLjHJqq+CdSPum2WcTUqUv36dn1Z5Ho/i+PSLlYWmRpG6KW/nUHjzxZ9ptHLfecHp6DtXOfHj4M6n4WS91awd2lso/Me2zlmXsw9jXH6F44m1zRYWuoJT5ny4C/6s45BryJ4OSdmfaUcyo1oe0pu5yfxN1m5vtai+zkszKN/zY5J9O49a42XwlqWqeIr680828cbQFJoc/u4lG1iwHU53E+2DXVWfg/UP+FswssjXGm3EZJDru2NnaQO/8QFerfCj4cx3Or6tZlY5dRuLlI1jiH+rJyGTPp94fjXu5bhup8DxJnF3yXPqv/gmr8M5PFfiLw7qAjZobWyBluAuRImMbT9SDX6M28CxW6gLt29BXz//AME+fhzF4N+DcLRqvlTORFgn7qgKR+DBsexr6ETcU5617koo/N6kryG0xhg0+myUGZG44ppGRUh5FR0AQv8AdpjjK1K3OajoAicZWon6VNUZ5FAEDDDVEy9qmkqN+tAFd/u1HIM1M3OahcZFAGOnWpou1QRnJqeE0ATJ0qaPqKhTpU0ZwRQBOn3amT71Qp92pk+9QBKnWpE6VGh5qRDxQBMOtSDrUdSA4NAElSp96oqlU4agB9SVE3SnqxNAEidKdUavika45YcfL1oAsMQBzVa7mht4t0hVFX+I8Cue8efEa08Mac2ZEkum4jhVvmJ96881Xxpe32lTXupXGyNASkIGFHpmunD4OdXU5q2IjT3Pm/8Ab51w+OviSyWcgtrOCLyTNISEZ93r/d4/PFeFaOJ9EsLy1aSGOzjkVJblojM5bpgY9VFes/H3SpvGVtqlxI2YHKFUC8RqDuP5nFeW3tp/wj2r7nkhNjqEiw7VcoFc9N2KVbDum7S3PVwuITSaNj+0ZTF+4um3QyLHbqh/1hf7vA6Yz3rvfAk0mn3eobvLdJgI7mUnYxfH8I5x/WvLNL0SNPFd8tms0aadAGKrIdryFyAd3sB071rXHi5vCmmW2mzautqlxICkzIrYDDJ65+bj0J6V5NSm3pI9iOIUdY7noWsaev2WSx22yyJLmRA3LkDOD3HbNfOPxf8ABlr4l8TandMsLahYW5lWGRj5SArjeR0IXGPfdXqEvxf0K6uNYTTb/ULm8hc24HlkZZl3M6lvvZA5PQc15j4th1LWLYagkZtTestjGuQ0ckcgOzr34Xg9cV5GIpckro+gw+LlWpqk2eV+FP2A/D/xhvpNW8YWqX0LeWUi3fuy2E3OAOufmA9K8H/aN/4I96/4V8bf2t8MPEVxpemtIzG3W5eMQZPCgA9BX6PfCrwbJ4I8CRobdWuZONzqGVCf4iDxx6VjmC8n1mZmVmjkJOFyVz6jP/6hXmfWpwlo9z66hl8KlNKsk0j4M+GXg/8AaF8FahZ6d/wsZre60m3KRQ3Lho5sdBngkfU5r3/4S/GP9oiHxBGNY8IaXr0WnxC4S4tbvIcgYHXueuK9s1T4S6P43MsOpWkEnmjYJGzmPPpWd4H/AGSPE/7PuqzXXgXWJbG31JvOltpENxY3R7Hax+U/7pFdcY1rc1KQ8Vk+HlDmja/Zr9TmdK/bq8YaH4j8QQ33w98RWslrbqA8MXmrHkFju47knpXNfED/AIKu/D3S9K8u81hrK9kfyplmtypg534cE5+96V3ni3xf8bPBN5rRWDwfqDXi71WSBkjtuoHQkt0zzivkHx/+w5rPxhkuJPHt5b+TeTG4Mem262yK7HcTvOWI7Yrpo4is7Rqnjf6o1JtyUUu1pH1Af+CpvwVuvAUkjeONKXzo4oTiUq0hUFmOwj5TjIzXXeBP+Ch/w5+IviS3sbXxZpim8tWeLdOrbiE3Lg9Pupk+lfl78Qv+CTGhrfsunaxd/aclid2UQZ4rj77/AIJn+IPCPhGa/t5r66vrabbCEG5Z1Y4OSOVBBxmu6OKhstz53FZBWhU5atrep+q/xC+MGk/E2xmm06eO6VGVYJUIaG6gL7VAK8HGR9MVyNxpzx6XrENqkccn2lpogo4CMxxz7d68K/Zc+AfiL4HWt1CGkg0u3dHMLT+YsUWxgvLdMEu+PQCvpbS/DbSJZ+TJ5kkke7zcBlkjaP5VJ7N3FcuIj7SaaPSyetHBwmpHIaj4eNrqliWk8y60NleZUYKrmbnaOxy2049q9i+FnhC98O+N42QQzTa26WiNEm1nl5+cf3euT61Qt/Cmm6tp+LhZrR7x1uRFIo8wlD8pY+mADivpr9ir4JyeMvE8erXDRsmj3X2uFHzgkrt4+gBr3cLBQpnxuZ4j2lRyPrX4F+Bx8Ovhxp+kL8y2sYy5bcWY8tn8a7Go7WHyYscAdBipCcCrPHI6a/SnU1zxQA2o6kJwKjoAY/3qiqVvvVFQBGetRnrUhOTUdAEL9KjfrUjnio3PNAET/eqF/u1M/wB6oX+7QBiRnpU8R/nVaM4qeM80AWYzUyHgVXQ5IqZDQBYQ8VMhxiq8RqZDxQBOhw1SxnmoUPFSKaALC8rTw3y1ATlKSJNv8TH2zUuQF0HIp6HIqhc3iW8YLybFAzk9BWHdfFLSdOlaM3SyPn+HmtIwnJ2iiZSS3OukbCetZmreL7HRY91xcRw7RzuauI1zxnq2uRNHpbRwQ45lZstj+VctqHw31TUszXE3nTN/Furrp4Pm0qaHLUxS6Hc6p8aoxH/xL7drhjwHb5Vrk9Z8WeIPErsBdfZ0P8MPy/rWdpOmTaJdC3vI8g9DXW21hDFDu8v5mHQV3Ro0qfwq5yTxM5Kxy+ieFZrq/Vpz5rqNx3HdWR8TbhdTvXsY5P3dqAJNvduDj8K9T0rSFtrBpNo8wrw2Oa8n0PQZNXnvZ8MZJ7uaRs/77KP/AEGunD1E5X7HJUvpc8k1VHudWubOOHzPtMeMNgJwSe/SvPrTwtqFlMy3SwXCQxsoeRRsjfsV9SB3r27x14Ma1uJGi/czSBlZu4A5wK8v1bUINLEfnLcrJNMFQMD8w7kjp+JrHHU1KpzI9nA1rRszzHUNIv5dNtbONpLc2gZ7q42hUdlcbdw/iyBt9s1y+v6qvii4s5LO3k+y20Q8j95gzEuFBx3I3E/8BFdH8WJb/VYLqKPy7aN5jyisfIH8PfnP3qZ8M9Cjh8M6arQyWv2c4ieY5ecx5wvJx8xyfXFeHXpvY+hw9SLV2a3wr+Cej+H/AAfeapeQy/YtKZso78yOw4YHtuOfpVzUdEtvFt1pLQ6W0tjFKk8e07VE6Efe9doDY7Ve/tybxfC2kN5KWV9JEzIFKxTxryyj0bpj8a6rw1pMkUd0tvJ5NqpUCM4wicg4/AdfevJrUeZHqYXFOFQikv4dT0NYmjdBcbogCNm09z7Vyk2itpupWdnDkW7uqgt83Pc57967m2tkudMuIbMN5/mgPKRuUKTzj8iK5yyvme8vLXDS3qKjxrkEQDjn8cD86+dqUnTqXZ+h4fFKth+SO5cHhXZLIrQti3Ys8n8RGSF2Adc9c9AK0rbUda8MaSrW91KtvKpOG+ZQO1W/hvqaxXtvHNNI8jHzFhkA2gchiPx28eleg2OnW5tFXUI7VVaIFIgOF6kAex9+a9CnhlNXi7HiVs3rYafJUXMjwXWtQ1HV9dnjkwi3jBd+z7xAP+Jrz/4i+HWgGGmG64JgjHO1mH8q+kfEaaf4qsEFnbpDFES4uCwVQ319uRXC/Eb7D4Y8UWGmzW8MkN44YOvUSFQGOPTbk59q0lg5dZGkeLqsU4xifPMPwsXRPFsFjeeWxv498D78x3JPVScdas+BvBchu7ySGOSGxQSrtQblcAHCkcngg/lV/wCNVxHd+Lrf7DcKqlwdJER3SJlAhmYY9c16v8BHhtraNJ49zXBbzUlUbHJzxkdqz9nHnUYmFbFVPYvEVevQ+d1+EutweEdUs4ZGij1i7gEM0jE7sRgFckf3cjPtXpngjw+J/BdxqEN8szJEDCkMZHmABgc9gu5cCtD9pfwnNp17Cui3TR2u9ri3t/MKGIorlkAPGSTwD1ry/wAP/EybQtet5ls1s7WGyCvYyDcx+YoFHY7m3MOvU17FHD67HxuJx0mmos9yg8OKfCsclxExeYYRg2WCbcYB9s8V9lfsK6VJZ+DGWSPKEDEmSccDjPfrXyF+z94cvPjb8VdF0KOCW40+4lWW5O7b9mgTmRj/ALxxj64r9K/C/hq18I6Tb2Njbx29rbRiONFGMAV6NRrZHiSlJr3jSI2n2/lTXOBTqYxyagzEpjnJp5OBUdACOcLTKc5yaY5wKAI3PFMJwKc55qNzQA0nAqNjhac5qNzQBHIeaic5ansajc8UARsc5qFzxUjnioZTQBiI3NSoeKrRvxU8UmaALUbZFTI1VY2wasI1AFhGxU0b55qkJ/yFVrzxRaaWm6a4ijxyQzUtW7RVxOSW5tCTZTvtixr83bmvOdb+OVrEWjsY5Lpl4Dj5Uz6c1ympePtW8U7kluPIj/55wnqPeuyjg6k99DCeJhE9X1v4naPo6lZLtfMXjYnzN+lcjqnxxkDbbCFju/jl/wAK85vLRdJvUleHdH6uTuP412GnyW89mrQxxjcMg4yRXfDAwgtdTz6mMlLbQz9Xvta8VXu6aS4mXqAuVUZ9KnsfCDJhp4pFz6mt/Rru4Tam5Wx6V0VmIr2HbJjcKp1OTZGfPKatcwdC8ItEhmt7uSBT/CpzXSaMGtlKyGSRh/E5+9VGdDoc2RG7QnqRWpBMLhY27HpWdSXN7wRunqGr6HHqq7ti7h39Kp21gbNwrBm298Zratvmlx/DjpTnjt1kZmbr2rGM2kauinqOgh26YwP92uL8CaZnSpW+6ftEvzEdf3r13Ycf2fxjGOtcx8MLWZtJvIblds0N5NgH+JS5Kn8jRTqOMWxVKfM0vI8y+OF+uiXNur/N50h4X2HevLW0CG+j1C4lZWjZVVSzACPcMnA+h617P+0p4Qe68ONNCMzH5FGPXrXiWkQQXtmthLE5uFBjZR0cbWAJGfavYcPaUFKG5nRrOE+SR5kdIaeNnvtnksXaFG+VlRc7ST3+tea+K/FV5qXjK1XTsppVqTs3cb5F+UbQfXPWvWvFVi1nazMSJJoPMjWMn5GByQDjoAeSfSvPr+/SHWxbYjmkt4TJPOkOY4RwSiZ6tjnjtXi16Lkj6DC4iN7M6Dwz4oXZ++3XN1cJJ9mhSThNuBux+Ir0zw3okmqaE8cNxz5ZcyYwAOVA/DOa+WNQi1rTfEdrefLYzQvIsNuzHOHKlVIHToSSfavrD4PayupfDNl8pFv5oMiN/m8kLxke+elcMqfu2O+pW5ZJwLPhHRmudQgt0j86HyXluX6CRd23b9Tgn8aypvhUt34hRrGM21rLEHkaLhvLblQx78V33hS1uLLxB5c0Xk2DSKS2cboigX89wrpr3TVttJvL+W3Wzs7feVjGPMcjGFyOikDivLrYRS3R6+DziUJaM8b1vRV0eee40+ZkmhKKC65bgAHH160zVviBdRNNPcqixtAiRSK4bcfu7m7bs8DHQV1Wq6c+qeCPO+z/AGMXEwlYkbT5Z4wxPXIrz7x1Zk6JHDZwwyWsFzho1H3gHBIPsOtebKLp/CfSRrU8Qv3ha03xdFoujLZBmkWOXJi43KrdWx3xjdj3NYXxf0u6+I2uacsO5ZJmYTOiqWxhj8jDkZzt/GpbTwYjeKdN1yOZpobiNoUhGNzFyUUqO3GMn0Neh6d4IWy1VUikkW+mfzyFG2OBFGOOOAefxrajKpOPKznqQoUJe0vc8qb9nK61LUo74oxuvNRZJkGNkZIIAHoN34102haDBpVjeW7Wm65s7lmK4Kj5VG7nsCDkfSvTtImW+e8aYObdQ371fQAljx74xnGMVyvjHU7bTtKZo2fzNWl+zMCfmyysxz+Ar0sLhI82iPms0ziVV8nQ8J/b+1JrbwPo91ZhZNStXS4QbtvmRspjLH6blJrwD4bvea/q4t9SkZrG0CK5VSoLJg9f6+9eh/tceMm1n4taf4ftZISdGs/OSN3xCwMZ+QjH3lwOvfFeF/GLx83gfwRNo7X32O4UIIxCw84pJsbzZe4yQVx6Cvfo0FBXZ85zObUUfcX7En7enwz/AGd/HevQeLLi8gknWG1t7+GEXEMSLkspZfmHJ5/3a/Qn4UftI+B/jnpSXvhPxJpOtwsM4trhWkT2KfeB+or+bTwDZ3up+IJv30itBG0u/dnzOpznvmvcv2KtC8Qa78X7HU9BvL7TWtZQzTWkhjOc96I5dOp70DavOnD4mf0Em7VzgA/Wn15H8IfiDrVt4ZtF1x1urhgAZUX5j7kevvXqFnqsd/bq0bAg9a4q2HnSdpHNTrRqK8SyzZNNY4FG75c0wnJrE1Co3anO1RM2TQAhNRk5NOdqjc0ANY5NRSN+tPdqhZqAGueKjc805m4qJ2wKAGO1QyNmpJGwKgdv1oAwo2qZX564qitwoNc/4s+JFn4ZRxk3E2OI0PT60oRlJ6EykludfPerbJuLAY9a5rxH8Y9L8PRsom+0TqOI4jnH1NeT+IvGmueOJdiySQx5OIouB/wI1XtPhjql+iy+Xt9VJ4x616dHAQ3qs5J4p29xGx4n+NGsa6ZDDcfZYEGdsfBP4mqulx3GsWqTzSSzbu/3qZeeFY7awZdsjSAfP8uBXU/C2y+yeHtk/wA0bHMRb7xFek40qcL00edKpOb1ZjxaC7ygp5jMeobtUklhdaRMrsuUY4zjpXaTR28NwEVQ3HOasXMEPk7VjVsjJrP276om13YwLjw++taSGVlZcdfeqfhS5bTb17O6Yqq8Amu38LxwXFoyKqrhuRVLxToS2p85YVdepFRHEXfIypU20mQXWl3Fuq3dlIZApyVx271sWF211biaFl87GWXPQ1Jo/l3+jK9uu35OB2z3pukFY7zay7SPbrUSqcyaF7Pl2NyxlOoWOJvv45Bp1jB5bFdw2g8cdKRvlQBe/p2qxAFR1PfvxWGmxvGN3qXbcbVO7g9Kx7Z/teuvu+ZFQ4Ga1mP7j9KyNJDLqr/K2GU8+9TB72NasdUjbb5bNlVtq7e/asXwhP5HivUISciYLKpP/fJH4YH51tSjER+n51zdtIbHxLa3X8LTtA3HZsY/8eAp7ppBK6nE3PHOhpqensjAdMjjOK+Sfito/wDwiPxHuryFmVimI9v3Qff86+zbqFp7YN94MPTrXjfxj+D0Pia1uLhVZbjtx1rsynERj7kjDGU3zJo+XPHdlDJpUt0srKse03hAzsLAjj2Jx+VeY22gXUmhXEdlcR39608krSIqoI2JIDZ6ZC16n418Ialo/if7PuuI0mbLRgZEmPbvWNqXws1kC6bb/Z8LMJlgt02tOR13HtmveqYSnJPbUxpYxxPP9E+H72sMdpdXbXd9eMZZry4nUeSqnKqO5zz04rrvDnxyuPA3jOw02SGGb7UH/dg5EyhWym8dGGM+ho0Hx3pclpcWctrDDqEbPGASN0fGOvoc4/Gsnw18K774hatc6xdTrY2sCtFBEv7oQuMYJJ5c49K8PE5dKnuetRxiqH01oeuL4kS1urWZRa+XsYlfvKflUD6HGa6zXblJtJW1ljkaO4hxl3G1SOmSPX2rxPwHrRttIutJVls5rJH8ve2+NxgZz9W5rpn1y+kuYrS6jWVobTejRHaoy2Qp9SB3ryalHsdMa1ncf49STXRFprTMI/tCtMM7VAXgfgelYK+CGiPnKEM3lNDPEjj5Q+WL/wC8owK09V161ZY7qRkWbhfLI4cg9z9cfhTYfETXl8wtri1WG4RvOeJA5Mm7exJH8I6CuGWDTZ61DNJxjYb4a0y10rTrW3e1ZpLaFZN+z7m58gevYfhW54g8Yzafqn2eT7O11fhotqjlVPIyevY1XF0LaISM0qw/K8rydSo+nauS+JnxO0jwRpjatJNC0sjhYE28k565PoD+tdFHBy6HLXx0pE+u+JZNE0qGxt9yQzXW6RkHzT87sY6kbsCvP/jv8ZNJ+DXgu6vdQuPtl5DcebHDHtJEm0/Ljv8AhXz3+09/wUAl8D38kGktHNfSRsVjUDECjOSGz8pJByfavmHxP8aNc8deHb+61ee4mtY7RJoZGyzh2jyxH1OMjrxjvXt4XLW/iPKq1m5anUXn7TWjw/EHWNa1G8m1S8ul8yeNmUGM44XI6sDjOOnFfNfj74g6p4++Jl14ie4kFxfSbnhfLKBjAU56gCsvTPGMPiDxX4gtYt1vZtciS1uYxtkTKDO8dwSOnWuq+FXwx1D4peO7LR7NWurq+kCZCk555Ndc6KUuRG1CUUueWh7Z+xz4FuvFdjq8rKWZoBBGMZG5uMD2Ga/TH/gnx+yH/wAKh8Lx3GoQbpLobzxnBJzXJfscfsQw/ByPTLW+QNPMqvKMdTx2r7l8H6Amm+EY4WXy/JQ7QRyMV0YipGjS5Ke54latOpVvI1rPSYDar5fyhVwAPlxTLTWrjw858tyyZ5TGc03zGisF2hvmGRSLbvcW25lbP0rxZRUviLo1nBnV6R42jvVUTbYXPTn5a2I7rzDw2Q3pXnMBWJNsnz7eeKsfbriKL/RbiRV9BXJUwv8AKdlPHLaR37PnvUDlgcb64ePxJfQBtshkdT0b0qzp3xGSO6EN1H5LdST0FZPDz6G8cVTZ2DNgVGzYqC31GO7jV42V1YcEHOadJLjvXOdPmDtmo3bNRyXSqduR+dDPkUANdqjdqV2zUMj0AJI1Qu1OdqhdqAPKtb+IAuI2SydWB4yO9cwnhy41eVmlXZuPJJ61itpS6jqt9Iv+qWTCAHGAK0tIuL7QzvglkuLYf8szyQfxr2oUlBXhueTKpKfxM6jRfD0dhMu+OPgYGDktXSaLzbSIy4+bA+lYeh6gurQLJFsV/wCME5Kmuj062C/xEmuWtUvo9yqcXe5iarpO2We1K5jkjLJ9fSm+GNJ87QbZuVES7ce4NaniPchikX/lm/f0NHg5Nkl1byNgrKWUDup5qlVfIT7O87Iba6UftLs+eentVxIdu7ORtFaE1uoO3PDd/Sqtyu9VQZyR1qo1GyalNJmf4ZufsmpzK23y34Bz3rpL2zF3YFWAO4Y+tcMkzWmsmP3rubGQT2S89qVTT3zSD6dTmfCV0+ga7Np0jZVz5i56c9hWlrUkllq8cqYxnkdqzvHFs1nLHeJ9+FwS3cir93dLqOlRzL8zbRk1rvqTKXU34bxXijfH3qsq28A+9ZWlt5tlHnjYOD61oq3Kr+NZWVzSL00L2/8AcGqVg+2+x75qfzf3XPeoIkZJd3vxUX5bphUV2makj7F5+bJ6VzmsQs9gzJ95Zy49iDxW7JkxbqoJCr20v++T+dEZJasVbXY6nRbn+0NORm43AH6VR1rSPMLdNp7YpfDM2yNom/CtZrcTpg+lcnM4Sdjr5VOF2eT+IPhjbahqy3DRIWQ5Q7eQa1rD4f2fl/8AHvGXcYfeud1dJqUOyQ8cg1FY3TLL8wGK9H61UcFrseW6EVOzPlb9oL9iCSe9utU0O0Vry7cBDF+7EJzyT61wWq+C5fhpcWtj4kuo4Lpogojjb5SAwIcj+9kcn0r9ACBdWm1lX5h6V8z/ALXn7PU2vw3Ws6PbtNqnHyMNwIHt6e1elhMw9v8Aua33hUouk/aQ1Pk7x/aeIrN7fVNLhaaO3mkSQwN8rKGyM88sw6Z4o8NePvH2uQ3UDaTJEtyu15bpCsiL2Axx+Vea/E3xF42/ZzuZZtQ/tDRYbiMTROoMkbhf4SD8ormrT/grHYzWt9HrEk2j/ZYN5nkgEiylRktgEdeBgVpicvUNtjsw+IVRXPqHQtTXFvb6utxaxyKYmIfzQhHHpkA9c1tal8QPC/wx0iGG1Kx3EjN5aSZwT3596+RdC/4KffDLxboaz/8ACZadZvfS7fKkIWfpyNo3EZHc9K57xX+2/wCDUtLu/uJkvLKBl+xBrsN9s3Hkr0OB3rjo4eM9InRUuldn0T4z/az1DUdGuDDCYrNASyJKf3jdMBsfd9hmvl39oX4w+KPFU1haWMEi30iuYNqZSLdwWwehAGM1j6t+2Da/ESK3t9GtzDIZcbJJF8uFMAjAB7ZHXuDU3xU/aH0y00RbO3SW61aWAQySxIAevJWvbw+WyerWh5uIxipvTc8Ju/ha95em68UXUci2iOx5y13KWDEE5+o/Gp/jjqU/h/wro+mQqFh1K2V5RgoxUE7Q3vnn8q7fwX4QuviPeyQXFlcRQb1maW4THAOW+nFZ/ijwTqvxq+Iy2umWsk1v5ywxogJVEGP8M16s8GlHlic9PGRfvs+e/gd4EvfEvxD16zsreS4mZ4sbQSeVFfrr/wAEq/8AgnJd+Adfh8WeJLPJeMNCrqcrUn/BLX/gnfpPw1+LXibWNasY7ttSt4ntxIMiMqMEj8q/SLRdJg0i0jt7ePy44xgL2FeLmWIhRk6cN+5Ua06sbnBeKtBh0zxvYeSuzepUexru9FvfODQSLtk2AYPRvcVyXxUjaz1zTLlvurNgn6112lRJqMEczcMowCO4ryarvTjJkxu5Fy+MVjGrNxngD0qBL0TDZ+NZXiG/+1arFCvzFeT7VZgbybtV/hIxWLjoU0txlhYst3PvZiu7gZqxKskLDygrDuKhvmHnME3KzDqKbo+qrexMqt+8jODuqfMHG+pOo/e/MuCapeI7D7XZthVLgcHpxUl7rJsZMSRllxncKdZeIbXU4GVXUtjGCeaOZx94qMb6GV4S8XTafcCPczRn5SpPSuln+IsGnwq1yrxru25rzbxNqcXhZW27ZLy4k2onXaTxzUN9PdavpqrPtZbe6iRsDGSTz/Oiph4S942o4icVZnsemarDqsAmjdXRu4NWt42/L0rzePUDYT/6OfJ244H3a3NJ8bmQrHcKsY/vryv4151bDyjrE9CniIy3OokeoWaoYrzzV3AqQaDNuB/WsDoFd6gkkzTpJOagd6APDNA00N533ep6VJcM2m3Hzn923Jq7HAq3AZVwucD3qzf2Av7VvVRXse0PGWrKKwyaPItzbsRG/JUdD9a7Pw3q631usm4ZYdM9K5Tw5f8AnQNbSqCyEjB7irUejTWMhurF84+/GejVjWinua0r81jstStDcW8g7svArHhuVsfEUE3/ACzuI/LJ9GFaWk6mNQtFf7rY+Zf7prK1ywa/09ljX57eTcv1FY097M3qaao64nzIVyoqB4sDIqHQ7/8AtDSoS3DKOaneTZ9CaiN4vUcoprQ5HWk8nXlbpk11+iS/6Ki+1cr41iEWoW8i9CeT6V0WgSh7ONs9B1rrrO9JM5Yr3y14hsFvtOdTg7hXNaHP9kjazY7uePpXXyHfEV9a5DUbd7TWFZV4PX2FKjJ2sysRHsdRpQ8u2C/w4qzPMY5F9Paq2nSAWSj2pNQlDvEvvzS1buTG8YmsJN8C06AEt9Khtm/0aPHpUyMB/hUSV2bIsF8xHFVNMPnfaFz91ulWbo/ujtqjoa77u7Ufe+X+VHNaNypW5kmaunM1rdLIehGMGujScFFx/EK5HV5Ws9OeT+KMbvyrf0S/S902ORT8roGB9c1zVbt3N6bs+UZexK9y2761l3Nr5RJ9K0tTYx/Ntzz1qG7TzoM8ruqoS0MqtO7DTtSycHtVq8t4b+D5lG1uuRWdpiGFeffmsb4t/G7w18E/htrHiTxRq9roui6LbNcXd1cPsSJVHP1J6AdSTRJNv3dx09Y2kR/FTw74O0zwTe3viZNJXR7VDJcyXyp5KL3zu4r+aT/guL+2d8M/jF8SNQ8I/B3wbb6Bp+l3Tx32tiLypNQYcFY4x9yP36mu1/4K5/8ABYLxz+218TJNA8JSatonw90vEllaxhkOpZA23EpH3jgggdB9a/PfWNUvL7VZruRln1CZy7zNH5kjt3JLZOa9jC4acqTbldNd9jGVSMJWSs/Tc87k0jUvDV3HsX5mRZVO3nB5Gc81+rn/AAR0/YN8I/t1+C/7P8aTaj4a8UKhOnrMuINQhjALNBngkbgSvbJr81Pid8PfE/w312xh8UaRqWjXWrQx3VuNQheL7RFIflkGQMqR6eldz8C/24PiD+z1qOjp4Z8S3pXwtqI1LTEkkIitZhgPtHXa6gqVzgg9Kzw01QlanK7+83rUpVYOL0P3y8Hf8EMvCvw31VYbe+mkBQK5JO5wOmO1ddZ/8Ev/AAP4I1WS4urFZmU5iaUZ5r0b/gnn+3hoP7ff7Oui/ELS/LttahjFrrmnq+WsrkcsMf3SfmB9CK9Q+J142pzx+X911Iz/AEr3KeYYlyUZO34HzsqMUfNusfs/afrt/a6bZWUNtDIwtppI0AOzoe3cV6J8Mv2NvDfwp15vsdnHu27lkZeQc/Sun8DafCdcaQr/AKlwPyr0VrgXeoXEn8IQYPvVYnGVV7t9CKdGNroX4ceFIdG8R25ijEa/Yjzj/aNd/Gd9xIv05rF0W32azH/0ztV/U5rW0a48y8l9jjFfO4io5NyZ6OHWiRy/xo03zfD6yD70MiyfTBzWz4XkY+HIXPJZMj8qT4sQfafDU3y8gVF8OZftXhaINwMUe0vQTL9n+9aGWOlk6mZmX5sdamQb7r5u3FaL/Jv46DAqhbgtef7Jpc1yORuVh99D50e5eD61mWVp5Gpu3QYreljyjDBzWe9rulB6+lKNTQr2fLuOkt47lmB4J4qqnhm3ilZ1UK3qBitFIljjG7jmkmGEyPu9RU3ext7JJXR5j4ptftfxCtYv4Ym3H8K0tRWaHSbd41Vo/trTzepVWP8AgKomT7d8SpXX7sSMMV0UGoLY+B47hlLMrEL7kkgfzrorOzijnpRumWLJrfV7BbiKRTHIMj/69ZskaRyMI5I2Ck5UNXH33i5rPTLxIcxiW6KAqe+ADj8ao6LPcaVfrMjNJHHHiUfxMxqo0Xa7FePQ9M8O+JprVyrfvIs4A7iuss75LmPKMp+leTXeuSaxax/ZVkhlU/vOwQevuau+AvEC6FfXlrJLI1sF8zLtuYHPJ/8ArV59fDN+/E78PiLe7I9NeUetRO9VIr5Z4FZW3Kw3Aj0p7SZFcJ6B49oGrDUrZGX5lA456VvW67k+8Oa8+8OzSaJd7efLbqP8K7jS78ShG+8vT6V6VSNnc8mO5T1y1bRrpbqEH5eWHtW14e1IGTap3Q3A3qcZGe4p1zGJoTuVWXpzWPphk0bVTAW227HcmexqZe+jSKUJXZ1Nqjafebh8scnLe1Wz8k/GNrjP1qnaXH2g7ep9McValTZGrM3EdY36m/kN0e6NlfyR/wDLNj8vsa1rpsRfqay3iVNrrjd2960DLvtxu64qZS2FFWVjE8ZoW0xJwu4q2ce1aXh6RpdNj27RwKZqKfbdMkj4+ZcVH4RuN+jR9RtGD+Fbe0fJZmPKlI6CO4+TbisLxIfKlV8Z6CtWN8ntWfr6q0K/7w4oou0gqXki9psu+1Xt0pl1PvvUXoafpiZsx9Ko3lwRqcdaq3M7GcrpJHSWpxCnPOKnU89aqWzfuU+mKnDba5pSZ1JIsytmP8OKo6DciHWLpTznb+FaBAEO7np0rE0eb/ipbzPA2r3op6xdyZfEmbOqr9rtZFGNrIQRVP4Rau174ea3kb57CVrc/QHj9KsyvmL8K5T4c6q1h4/1iz2t5dyq3KHsSPkI/rVcl6Tt0D2ijWXmek6gDPbDb2ak3BodrVDBe7oiwxwc81zfxJ+Kek/DPRpNQ1S6W3h4CLjMkrHoiL1Zj2Arlp88nyxOipKMVzSHfEj4kaP8JfCmoa7rmoWum6TpcJuLm5ncJHEgGSSf85r+fX/gr7/wUP8AFf8AwUH1O/aHSfFWg/s7+H7prXTryO2aK31/UP8Alm88nQKfmKjkcDua/Yr4h/AK6/bJa31H4gwyR+EIW82x8MCQiO5I5WW8wcufSMcDvmofjX+yx4Q+L3wc1bwDr2h2knhnUrb7I9pFGqx26/wtGMfKVIyCMc17eFo0oL3neX5HnvFPn1Xun8u3wS8TXXga9urie6uZrho3gtbJ5WaGBWyCzjpwOijvU/hF9Y8K+KxqWiKv27SibkyyorRIvQl9/wAuDnGD1zX2Z4s/4JnfDX9mf9tTxZ8P/HnjWSHR7PQ31TQCs4t7i9kl5hjBYEMy4OR/F7V82/tCaJD8Cvil4m8OWcw1TR5kW3k+f5pVG2ROV43hvwrup1MNSnPDUV+8aUn2a9SpU6tSEcRJ6J2OP/bf/bQ8Sftra3pN94mttNh1TS4ktFawXy7cpGu0NjOAx6nbx7VJ+wv+wj4u/bS+LMPhzwvZNeTbfNu7k/LDaxjG6R27KOnua808A+BJvGfit7URysZHAjiK7pXdj8sagdWJwMCv6b/+CNP/AATntf2Hv2TLeTVLNV8ceKolvdWdlBa2UgGO3z6KvJHqa5sPRo4aH1lrd6Lz8ysZiKtR+zi9erPjf9mj4Ca3/wAEVP26fD2i3uq3GofC/wCJlsumT37JiFb0gbd46KRJwD3Vq/UyW0AtZI/9ZtwYmz95T0rL/a5/ZP0f9rD9n/VPC+qRiOWVVmsroDLWdwvMcgPUYOOR2rgf2MPG+teJvhh/wjvircvjTwHMNJ1dHPzThB+7uB6q6YOR3zXo1ayr01Wh8S0a/JngxcqdX2c1o9V69j0bSNCXTNYuW27VaPfgdjW14Lla+S6Z1OPNAX3FN8QCP7BPcKflkGAfUY4q98OLFo9Pt1P/AC0kDfhXJWqPlcjppx+ydjpm0+I58YwsCjH507QJPPvpj23dKg0GXdrt+3faB+lSeGFzdSc/xHoK8uppe56FFXsWvGUH23SZl/2SQPwrD+E7s2jNGzYMZP8AOul1dcWrfTjNcp8N5fIvLuDkFXNTTd6TRdSPLVT7nSK+9pB+GayxKYr8p0A5rUEIjldgdw96ybxSNRB3cNTWoP3Xdm0r7kyO9Vbr9yvHOKkRsRD0psib1Jz7VlzOxpKKlqNik81OnPvVfVpxZWTMf4Vpyt+82/hWV4yu9ulSc87aKd5SQqklynEeEY2l1/ULpvugMMmtueHz/h9ZxnrI6kD8c1Q8PWbQ+HLyY8lwcY+la0YZtK02H5cKgkPtgcfzrqrS9/Toc9GNos85u9NOkWTyXUbM0Lt5at0d2br+Fbz6Stm8Ea8TEhmOPvlqq+IZxrWvwJ1t7dsOPVicCuq0fThc3kcknzeWqqSfUVvWrcquzClT5nYqwWEenWbfdMjncQR78V53ZalJqXxO1SMP5dnBbrbMv/TRhuP81r1TxIYbKzlkfjYhZj7Dk14z8MJ213TbjU/49SuZblD6puIT/wAdA/CsqLck2zaaUZWR698HNVmvfCvkz5Mlq5QFjyV7V1zScV5/8JLzb/aEJPMbpu+pWu5aTivJrRtUaR61OTcbs8UWyaY9Nv1PStjQp/sxEZbjPBPQ1XtAYj93PvViOAyxfN1zkCvQlLm3PP5JLVHQQ3G9VG7K96Zq+nLexfLwy4I9qpWFwybVatJJiNp4rku4yOhLmWozRJ2g+WRvnHBOa2FufOj29cisa7GCGXj1PrV22n8lBycfypy11FGLWhfsZd8AZh90nIrSt2WRW3AYxkcVhW87RzSRgfe+YVqaddb4vm64rOSNYiwAC2mJXcuTVHwcpimuoXY7Vkyg9ARmrkVztjlU9CTgVX0cLBq0j/8APVQTn2rRPQya95G4Nv8Akdao67B5sP8AunJFWvNwB71HcHzo2X8MURlZ3HNX2G6Q7GEDLYxnFV9SjKXMTn86tWbeUvf0qPUD5uNvNbRqLmuYVIaI1LSc/ZkqxHJk9etZ9o2YRg981YVyrL61jLe5qr9DQ8xgnPTtWPp3Piq4B4+QHr1rVWfMOPbvWItxt8YKo4LR/wCFVS6oKm6OjvF8tOOmK5BI4dF8W215JMkayM8XzNjlhmurum/cr7CvOviR4Mt/HXh+60+6VzDM5BKSFGTPRgRyCK0w8VJOMnZM58VeLUktUc7+0B+3R4f+Dt8NE0qG48UeLLn5INK09TLIrHpvwMKPc81yfwW+Bvjn4m+NB4++KErR32S2m6Ij7rfTF7EjoX969E+Af7OvhH4PW0k+kaarahcNumvJz5txMfUu2TXqyu1xCRs2k9SD1/Ct5YqnQXJh4+snuZxw867560vRLYr2UMSaYsSovy9Pr1rzH9or4n6F8Bvhf4g8WeILiO10jQ7SS8uZD94KvIA9STwPrXpMCi0nKAkD371+PP8AwdJftXX2k6B4Z+D+jzSRR61u1TV3U4DKpxFCSD9XIP8As0sDFzq8iNq1Ncl30PgXxN4g8Yf8FnP+Ch8k2nXVlpM+qpN9muLjEcdnbQIzIgY9FVcAnuWNeUfte/Avx1+yJ8Ur7wX4t1zRrvULWKORrrShHMpSVdykSqoPKnJ7jNP+H/xvb4HrpWpeFLF7PxTnypZHj8+I/IQ0kaj7u7uDmqfxC8S3Hxb0zWPE3jbXNSudQaZLSN40WV5HK++AqooHA7cCuulKrDEOeIgowtZX+Jv07HV+7lRUactd/I+7/wDg2/8A+CeNn8WPF83xc8Q2cd1o3ha68jS4phvW5vepkweojBB57mv3wm/49OnYV/PL/wAEE/8Ago9d/su/G+1+GGvXkf8AwgXjS5WISyfd06/ICJMp7JJhVYHgEg1/Q1LIq2i/N2HOOtRmVSbqJSVlbQ82hRtdp3GtJ5Cou0fvOK8D/anl0n9nLXLP4nyyLZQxNFp2t7Fx9qtJHCKxA/ijZwc9cZr368Rd0bddvNcD8cfAWm/GGSx8PataJf6XeCU3UL9HQIQRj/gX5gVz4OpGnVvL4eticVRlKm1H4uhXvpIdX0dWhkjmt7tBLA0fKsh5GPwrsPC1mltHZKq9I85x7V4X8CvDmrfBi6m+HWr3DXUWl5fw9eyAn7ZZLyIWP/PSMYB9Rg17xDP5Uccg4AQjH904HFb4qa2i9Ohnh4veW/Uk8MSq8+oSf9NSOPQVJ4Xmzdy49TVXwrtTTLiTtK7NVrw3tBL+5GK5JtanbR0cUal23mRsv3uork9FX+z/ABZdR/dDjIrqJ2wucYX1rl7wK3iXevPFTQfutF1/iVzqHlBY444rF1NsX0eG79a0ELbxn0rM1SYbtrf3sClTbQVLyRprJiKh5dw/DrUEJyo/KpJmwB61E5XNILQRjXN+N7j/AEAj1OM1vSS5Fcz4wl3iOP8AvH86rD/GZ1vhKbSmw8ETFfl+Q/4Ut1e/ZrOQ7iWVREpP4H+tVvGtz9h8LRwt8rSOkePUFhn9M025Vr2yt4CMSSOXPtzmuj+8+5zyfRbnPyIbacKq5+0HcTjoQRXoVhALe0JxjjJ4rjXtGh8XRxNtaNV8zAPQjvXRX+tH+xwy9XJWpxUnJqw8Kkoty3PO/wBpLxhc6d4AntLGRf7S1yVdPtMN0eQ7c/8AARk/hUnhnR7fwv4agt4FxHbQpBH7hQB/SsMxf8J98VHvVPmaf4VQ28BIyJLpwN5HrsXjPqTXSa/Omn6ZIqnlBjPvXRDSKgjKTu7sy/hN488j4qX+nSvhL6LfH/vqcY/KvZjLuFfLNndSaX4xtr5OJYzvX3KsP55Ir6S0nW49U06K4jbKTKGX8a4cwpctTmXU9TCyvCzPP0mMr/KwAxVxJG8tQDn1NYemXfO0t/8AXrYhfhdvy+uaqTsY03daF6A7Xz/OrtvLuPNZ0L7nAPpVy12881hLXY2Wha37vcVIkmCtV0i3uef/AK9SQp843Z9qRRc3+UVY8881es5vLdl/vdM1RkgWaDH8XXOaIJ2M0e48rxmlugNCJi2Mj72ePSi1k/fFhjKnFM85RKvrn1pbWT/SG+tMlo0cl8fw0q8seajV9o96PN5oBkyyZ449M025/eL6H+dQpNj8+tTRHc+DzxVRlbchq5PYSfugKm8/9+q4qurLGePu06FwZt3XBqWC0RoRtmP2xXO3z7PHdntbAaIjFbMNzlfx9KwNbYJ4201u5DqTWlHewqiukdmJvMtzn071zMm2a5vE6dCK3DckWx57Vz+5U1w4/wCWi1WHe6M8RrY0vDT+Upjbqv61vWsrB/6VytvdtbalycbjxXQxXKiMNn8qmtpIrDtNFXxhq8fh21uL64kSK1tYmnmdjjYqgkn9K/ly/bd+O+rftrftb+NNct3+0JrWtldOjlBKmJN6RIv90mNc474r+hz/AIKh+Kta8O/sHfFC90FZJtSXQZ0j2uE8pGGHfJPZSx9a/mD8IFrh5re3uWtLqbD207OUKzISVG7PBZSQDnqRXsZdRl9WnWptc6atfp5mc5xVeMJ7Pc9y/wCCi37Hvhn9j3wV8J77w/45tNa8Qa0gfVIYC8d1BcbQ5ZegVFLBBjkkE14Z8RPiJqXijwN9juvJn/eCQyC3RZSy/wATMoBJweSa5zxF4j8Q/wDC0re71i4ur2+jUxKupHz28leCPmyOfX2r7I/4JufC34b+KZfGsnxMtZr7w/d6G1rAttOIJrGSZgwbLEH7gOCO496KeKhQwcsTmLUpRekl38joqUZVcQqGFTSa1R8X+H/EE1g9vdWkjQXlmwaN1bG0j/HAr+o7/glp+1Of2wf2H/BHi64ljk1X7KNO1Ur2u4cI5/4EFDf8Cr+YHxFLpPgHxp4vsdHjg1bTbXU3tLGa8USSPaBmAbA6McD5hX7Jf8Gsvxma/wDAPxI8DtIFhs7231q0id8sscgMbgD2ZVz9avHVo4ig6qW34nH7F0Jcl9+h+vcpZ5h83ytjisiwgF549lb/AJ9bVV/77bP/ALKPzrRExim+98uKzvBcXm3uqXXXz7ohT/sqoUD+deK5WRpTtJ3Y7xt4Et/EdnCzExXFmwltpV+9C46EH36H1BqOO4uJLObzreaGSFPmZlwjn1HPeuhkdmj29R2rN8SSfZtMkHyjzCMYFOE5XUUVVpxs5EGio0fhobsjdk/mateHhsi+h/yarGVYNGhXuAAas6OT5Qz+lEvhbCNuZWNK4O9QvqK5m/RotbX6V0F62xvYVj6oublJOnrSpy6F1opmgkmYxWddt5tw0bDbtbIP4Va3+VCvH4mqTSf6Wc9WpQlYfTUvRv5cfPOOaSe4zjFQb802dtu2sylorCvLufb+NYGpn7brSJgbYx+da1xLsjZxwaytOImv5pW/hGM56VrTbV2jKpq0jA+IrNeeINJso/m/el/wVT/j+ladpKs11OB/yzwoPp61nIFvfEsuoSf8uMbRp7k//qq9pqtBZZf/AFkh3sPc1tKVopIyUbvmIbi2jjvZLn+IR7SfauR8V+PZ7bwvH/Z0fmX1832SwQn78hPL/wC6vJ/Cui8a6rFoHhy5upJEiWNCWZugGO9ch4C01tWvYtfvIGiRk8rToWHNvF/fI7M36CrhrHmkZyupWNjwh4Vj8FeGrWxUZaMEs56zSsdzOT7sTWN44vwy+Sp+7w3ua6m7vsws57ZCk9a4LxGfNlkYevPNb0NXdmUrbI5+S1MrwSBeNzgn0GRXq3wq1Fo7B7Nn3eSd0eeymuE0rSxdaXDu4/e4/A8V0+kOdG8aaeAdscyNCffjNZ4y0o2OvDVGpeph6bNi5Pzd62bW8ZiuO3HFc1FJsl5NaMGob9vYj0rGWuwUZ8u50sVxuUe3cVcsrjaDu/WsOyvty8sPatCKb8hXNyu51KSexsRXXHyt9atM5eL5MZx1FYfmsv3adb60QQvzCpcQ5l1Ni0u2SVlb0796VZ9l0eq9+apLd+cGZfvL0zUd3qHk7HY9CA1UovYd9Lm7JdK/lMGHX1p4ufLnP51mJLvQei+lT3E2ZA394cVNhmtHe5GN1SLNuP8AX0rPtyxYYqwjMh96CbF0Mo5qRLhR+NVFl+WiJ+R3/GgVjQW4U4p2/ef6iqaZxjGaljkK0CNGB12+9c/rp2eKdNbjAZsn04rXgl5U+9ZesJu1e1kb+Fq0o6yuTUehvxzZQfSsXUx5eqxv29u1X3m2Bc/pVS9iDyq3ZetVTtF3M6quhLqcR3CMcdeCa2bebzbLj86w7yLzYA3905q5omoslgyt82W/IVdTVJk0dLpn5y/8HFP7RGueFPgh4T+HOgMI7r4hX80V2M43W0SjcCf4RuYEn/ZNfhh4/wBCXw/aNNa6xperQRt5Uz2jv+5kz/EGUZBIwGGRxX6P/wDBx/8AtBwWf7YWj+Hv7P8AMu9D8OkLcLcFWjNyXJ+Xp93HPWvyx8NagftbRgJOJtxWF03JIw5UEfgR9cV6OHliKS93SNu3xXNeWE/dlv8AkeiH9lnxt4e+EemfEu60HWf+EV1gvHBqC2sjQoEcIC7YwqsTwSecVCdNk8c+EPsdrJ5eqWKkwwltn22POSgPd1OSAeo47V6b4+/4KX+KvH37N9n8H9Fhvrbwy0MNlHavcBtqJIXEagL03+pryrxZ4D1bwv4ZhuJltzDIMb7a5SbyX/usVJ2t14NaZfip4nDyp14qnO75U9bro/mPGUfY1U4Sco21aOX1jw/a2Nvoemw2dxH4gvyHd5CVUF5CiRbMfLjg59TX6Ef8G3Xji5+Ev/BRmbwtqUU1nNr2kXdhNFKCjRyx4lwQfePA+tfnJqd3qyXOn6ldTXTmGULa3Mn8RQg4Dd9uR9K+uf2K/wBrnUPFv/BTX4aeOLi1uPt9nf2dlqLqgYyIcxM7lQOu8ZJ5qoxnGn7Kr1T18zKpyyXtIX0P6ari9WFXk/hUE9e9HgtPK8O2rYYechlJx/eJb+tY+r3rHQ7rywVaRPKTHQknGfzP6V02mp9msY4VGFjUKB6CvDqbWKoxsWVYyDArI8Xz7LWGP+JnAwOprUtJMS/jWLrzLc67BDjpmTn2qacvft2NKnw3fUW5+conPQVo6UPKAVv0rNml/wBK4/KtKybc3oaup8NjOjHUmuJCW59e9Z+pLuxjoe9W71ixxn8aqXLZX/GsoytsbS3HSSF4lGDxVNk/f7vm+tWI5Mp7qMVXaX52ppkskEu0ZzTJLgNIuD+FRGTIqNzj6egpDF1KbZCe3H5ViXF4un6TNJkfNz9auaxeCaMRj7zHGK57xreeVbRwL1YgYralG8rIxqMfp6fadPhjHzG7lLlu4Uc1tyKrDyyu30qhosKJep1X7PCEAHTmrmoSBHMnZelTKTbsaU42jqcd8UVGv6lpmjbVeGaTzrgf9M4/mwR7nbWreyxqVjUBdo5HpWLp12dX8Uahqe3ckeLSEf3tvLEfUn9Kt6hM0EKs5G9uWPpWtnojmlqnJlbW9R3DauNq+lcjfSeZeSKemPyrqEh89JGboQa5uzt/tN7IuNzE/kK7Kckk0ZRjd3Zr+H9PVbFmdSV4YADriq3iHUFbWdHuI92UukBGfXrW5aR/YrPDY6dq5XxzeJpt3pM7YWD7Um4jsc1yS1bZ0U9JIZLZZbdzj19KrkSW8ny8r2rXWMyx5PHtUb2XmRtj5vQVMampUqL6Fa2u3XG5sd63NP1USIv6msB4GU8r0HWnaTeeVIy+hqpLmM4vklZnYRvnv2qvqG7fuQc+lQ296vlrViK4WUe9Y6p3Oh2ktSoPEM1nHzGR61Yl1dNU0jzM4Zxt59ag1y0Z9OlMa7mwSo9TXP8Ahu7Y20kYY+dn5kPQV0RjGUbo55VJQ91naeGte/tDRY5WH7xSUlHoRxWxNNuSMrzxiuR8OK1lqNxjO2ZVbYP4WH+NdIku3T/dWrnlG0rHRGonG5qQXewg1aiuPN+asGO92zKuTWlbS/L6ZqZR6lU5XNETDb96limCnP8AWqMU2T171MW27cVJpY0objfSiTEh/nVKCXa1TLLk0E2LiTso/HrVe/UtdxN1wfwoD5ps0u1ge/rVR0ehnvuXJ5sBRUN6+F9KjMnmEbj2ptzIDE2a2piHiYyQMvtnNRWUvlJjPyjIPHPNNSYRMVPVl/Kq00vlqoZtu1txJ7VUuyMpR1ufjZ+0/wDBjwR+1b/wVw+L1x8StRmtfC/h/Txp1pPBF54FysKKqsOgK72IB4yK/Mf9qX4Z6B8DP2hPEXhzwvrs3iDRdDutllqbReVJOuAQ20HqCcZHpXr37anirWvif+1b8UtZ0a/vkkuvEN6Li2tpWV7mJZnWOQKD8+AApA5GB2rxyPTNN8O6farrGlyahfahGZZ3lleFrZMkKFC4+bjJJzXTT9vTxD9pJSUkkordW3budvNRdH3U0+72MSw8V6Xc3f8AaE1lc2upKhUzWrBYnfBHmFP4Wwexxmtjwfe+XZfY2bNveAQvuP32zkfqByfWvWv+Ce/7FWn/ALTf7TmneHfEOpLo3g+SO4nl1G5fZHIEjLrGXHRjkenQ0n7cf7I7fsXfFpfDMfiLRvFEFxbR6hbXWnkjy45CxRXVicOAAep4Ir0MDLCqtLDxl+8te3Vf5HNifbpKq17v5o1fi58YvhTqX/BPbSfBMen6LceNNLuWurLUIyyahbtJPukjl4wQEyp/3RXz18NPF7eFNasdStLi4t9Ss7q3mVomwknlNuBJHII4+uKrXGnQ+INYt7mExM0Z33dpKQC4BJyn94HjI6jNdZ+zR8JJf2l/jVovhy0W00ibWmeJ3BENvFKEYpknhQxAHPGc1xYSKpylGtNuzu79PI3rycqd6aS6I/qp+EXjOP4j/CrwZrFvJ5lvrlja3yt/fV4lcn8zmvT7c4i+Zs55+lfKf/BK6TU1/ZC+H2j6/mHXPCNhLo+oReYHWKW3lMS4YcEFNp+mK+pJZfKTrmvNq2UmlsY0b7stW8mHYjtWL5v2rXZJv+ea7eOnNadm6i3Zsc+tYmly5W6kLffkyAKml8TZdSzUSzBL5l45P0rTtJNi7v51k2xHmFlHfk1fhcCP1xTqS0CFr6FiVjKu6q0qh+ppzTHy+DVdpKyiVuxwfaTVOZvLl9d3ep2kqlcy5kU9e/0rSMbsmQ7zsL+NOWQFG9xVV5OP71Bk+YZ/KpK6lOdf3jSf88zkelcvczHWvFcEZP8AqiZGx0wOldF4jvvsds23GTx9K5nwHF9o1jULglnO4Rhvp1rop3UHI55JOaidRpcYZJmJwdwH5VjeP/E39heHrqZcblTEY7s54UD8SK0be58m2nbcq/PnnpXnviXWP+En+I9no8bb4tPAvLs9cHP7tfqeT9KzpU7yuXVlyxSNzw5bf2JoNvE7bvJXJ3dWc8sT+dOj3apdSBvmjOMe1LqD7sRqcYwMelWtPt1ghz6jn3rWUko8z3OaMXKXKZniac2Nrtj+XtxWd4dsllm8x1I9Md6l8X3Jmuli/wAirWmw/Z7AeoHWnKTUPUtLVodqs+5fLX+H9a4n4tr9q8KTKp+ePEq4/vKcium+0m4nbJ4zXKeJJf7UvZLXH3VINaU4p7hFtS5kb8cm0e1SxyE/8CNUFl/2hzVy2+YA7hXC9DtHSR7CfyrNngZWZl45/OtaZWduB1qvdQbF+YcmtKdToc1SF3cWwvcQR85x3q7ZXTI+7v2NYkSNbSYHfp7Vdtr/AMz5SMYrZq5lzNM6GO7EsZ9W4rmr/TZdM1uSTB8mb5g2OFar1reNvznjNa0Mq3MIVlUg+tZxk6bNFGNTcztB1VUulLH943aujtsTSSIPukbhXn/iaabSfECTR4W3jwCvrnqa7fw/fxzxxyAqQ4xmqrJNcxNGVnyDDd+Trax+o/OtqOTcM1z/AIgiNldxTLj5Xx+Fatpdb4VrOpqk0aUtJOJqwyYjFTQ3AbiqcZPkde1PhJDVmdBdZiWG30qRGzVQXO3FSJPu9qALqz7RUkkivt5qmshGP50523Y/zmqjuTLa5I74k4NNkyzcUxpMvz6UokzXUZ+pIF/efNwMdaw/Fuqtpuj3l06/LDC8hOdv3QT17dK2XkMcf6Yrzj9p/XP+Ed/Z88cX+5Uaz0K+kDE8Ai3fFEU3NJGNR6I/mh+MOi6xoPxB1LU5v3bXmoTTx3FvcJKNxct99CcNz061h+L7XXNR0rT9U1SO+a2vEdLS5uV+ScIfm2HA3YJwSM1Y0N11G5MMzSSW82ZnijYhnkUZzn1wTX238Xf2yfg9rf8AwT+t/hnb6P4f1fxBp+ki10rUf7LZLizl37zKW2ja2Mjrgkk4r6TFYxYWvTp1afNJ6XS2Xds56eHdenKUJWXm92fJHwj+KHirwDo8914N1C4s9Qt4PJvI4gJDPBuJV9pB+6SQcdBii8vLv4xWGoeKvHOvak62kgt45dv2ie4lIyEVSwAUAc8gVydtZ6l4Qa1voY7qzkkJMNxtaMkjghG6HitXxv8AEzVPGvhL7BfLaNDFIJC8FssLZAxuO3AJ7ZPWsauV1Pbe2wcY+8/emrcyXbrc0jjlKCp121yrSPR9jzLW/CL33jaz0/SHfU21SZYrLbFtkkd22hSp+62eMc1t+E7zXvgh8TJIUh+y69o8xRoZo9w8xMhlKnrkZx9BVj4V/EdfhJ8UNJ8R+VNMdDu471PJ2+YjL0ZSwI4YKeetdF+2D+07N+1p8Y5PGZsDY3P2KCK4kiiWN5pEJ/evs43HI5P90Vx1pN1Xhqsbxtu9m+zOqnFKnGtGWt9j9vf+DcT4zan8Z/2VfFF5qsq3F3Y+Ip42YDAzKqS9PoQPwr9FRISmK/IT/g1d+IZ1Hwp8VvD4jhhW2nstROwnEzyB0L4yccIoOOK/XaNGPda8jEQ5Kjjay7Di76jtSuvs2lSNz93ms6ybydJRMZZu+aPFVwYoYYQd3mMAceneoop9vlryAvXPeohHS4pS96zL2nJmM7qvIu2P8Kp28gxnB5qd58jH8u9Zzd9TWOgNKFC59ailfg0kvzD05zUU02BUxERzyZQ1n3Uh81etWHfgjrzVWbLSLWlN2d2K2hdiwEzVa8f5g3TrUjsIIuuTVDUb3av+9+lSOV1qYnjLUfKtWH+yc89v/r1J4Ks207w9DubLOpkc/wB4tyP6Vz3iu7+2Trb/AMU0oQH6n/8AXXYx262ljtDDYq4A9q2rS5YqJhSSk3MzdSuoo4dsjKFwWOTgVgaLpEXh6e+uljX7VqkglkYdWwML+lS+Jbb/AISO/hs2X/RkYSuf7+OgqadTe3irH8oRsc9hVR0WpEnzO6HaZZlX3N94nNW7uT7LA30qdFW23crnFZus3OLeRs9RxWLlzvU3jHkiZN3ML7Uw3BA7itCV/KtG+mfpWTpNo0bszH/WHNWtcvPKtdq8bhitpO7UTGnpdsybi6FrB5udrNIB+FYugg3fiC7bsKn8W3P2aG1jH8T7jjtimeCP30l5L90seM9612VyIxuFnqGSFNalrKdwAPX1rnLW52XCr/k1r2M4HO7PvXJUidVOTaszcjkwP5U24bJqGC76d6knffnb+NYqWpryqxDNFvUBappkTZ521c37ajuolJyvy/SuqnLoctanfVDre5w1aVjds0q/5zWLbnbKwbjjvWhp0vzrzmqqR0MqOjOD/ab+Jkvwq8P2uqR6bNqVlJdLbXgi+9bI/Hm47gGum+Dvi5df0Xy+f3YV0OeoPNbWv6fb6rp80UsEcsci4ZXXcGB9a5XwVoUfg7xFHFCjR2kiGOPb0Tvt+g7VtGUZ0OVrVGbThWv0Z6J4uXz9JZ16lQR9am0e4EtsrZ+8M5qOb/TdLIPVVII7VT8LTf8AEsT/AGcrXGvgsdjX7y6Omhk/0cU77RsFVYph9n605W3dak2LQm3nO7tU8bbhuzVRHUYBp4kA7/lQBdim+WpPMqnG+Kf5lVHcmWxO77eRQr5qAyZbmlduR6VpHcz6FiW5/dncfrmuB/aI8JQeP/gd4s0e6vIrG11bTLi1kuZThIFaMjcfp1xXaXUuYCM+9fN//BT/AMe3/wAPf2DviFqmnXFxb6hHp+yGSL70bM6rnp71pCNSUl7OylfS+3zMeaHN72x+C37UX7P11+yb8edS8Iya5pmvTaSY5or2wJMMgdQ65z0bBGRziq/gDR/DOueLdP1CfUrPTLe2nSe6069UiOQrywjkGQVYgfK2CAaZO9t4t02TxJ441rWr661KZobdolWWeQrwXYuR8q8YA5NZngb4S3vxM+Kuh+EdFaO9vPEN3BaWTgbd/msACwPTHOR7V9RU5cZQ9nKo4zSs5LZ23SuccLUanNCF4vVLquxta98WNQ8U+LtVs9U1CS+02+meJIFYSW8A3EIY16LtwuMY6V9f/wDBPb4WfBOD9nbxxafFBW1CDxHcRRR38N2LWaxiiLGReW3KQw3dw2RXzF+1x+xT42/Yx8eDSfFukXVlYXkrrpt+dvl3yLjcV2scEZGQTkZrhr3R5fH2l/aNM/fahCuLqxRsSTYGBLGDjcCMZA5B+tceZYNQwsHg6ypw0bla+35XOrB1m6sniYXfRbHnfj+30/RPiJqQ0t2uNKtb2WO2W4YMZ4NzBdxHXK45FEHiuHwv4fjg0O8kjW4uzNdRMm2TbswsbdnQc/XNaXjeLRvD8zaPNpEkV0iDztSaZvOSZlB+5nZsBIGMZ4PNdH8JP2MvGnxm+AXirx/4f0ybUrPwjdpBeQRY83y/LaSSXaSGKqADwD1Nci9nX5as212v1Oizp3hG3y6H6Mf8Gt6w6X8VviZcfalUXumWwt7UH7yiVic+65Ax6NX7Xafdbk/vcZFfzvf8G6/j270j9vzS9PjvJo7PVtKvLeeHfhZSqbo3298EY9a/oWtbjy7T/dHGPSuDMLqryvsTRlF7bFfVZTfa7DHu4hyxxQjf6Wo61V0q5WXULi4PO47QasWJ865Y/ewTWPNyqxXxamrA/wAlOMtQFxGv9BTfO61g1c12JJJMHrVW6uSn92iWcBP7vvVO5m+X8aURknnFjzVeeTfMvNNWUZ60AqzdOfX0qgHXU3l7fmPrzWVrmo7YquX0yjJ9ua5jxRqBWFvL/h5I9KunG7Ma0rRsZCy/a/H2n24+by1e5bH8IHyjP/fWfwrs9dnkt9PwuWYL37/WvPfhi7ar4u1u/dvMjgZLBM/7I3MfzbFdtqV4DDyO2enWrxEbz9DPDytSv3KMUnkjLctt+Yj1qxpcRKtI38XNU7eL7SdvaT5sVeuX+z2vy8bR2rGUnsjSnGy5mR3E+ZGrL1q6GwJu706G7aQtITx0qjdP9rl/GtYRs9RTk+XQtWq7l3eg6Vl67ctNfQwr67j9K1Y3W2syx/hB4rnmvN7zXTDPZSeuKdNtsmekUjF8YXO/U1VSDtUgVqeDrYxafnPLVzcjfatWZt27rjPaus0lRBZqM/gK2rS5Ycoqe5//2Q==) ## Thomas Analyticsn-Tutor provided us with the support we needed to keep our business moving forward. They kept on top of our challenging data and provided an outstanding data analysis service. 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) ## William I would highly recommend Analyticsn-Tutor as they were extremely professional and incredibly helpful throughout the entire process of my complex data analysis. 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) ## Leisha I highly recommend Analyticsn-Tutor. We have worked on numerous projects with this team that have helped us optimise our marketing efforts and better understand our customers 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By grounding the learning in practical applications, the course ensures that participants gain hands-on experience in transforming data into accurate but also engaging, and meaningful visuals. Ultimately, educators will be empowered to communicate insights more effectively, using visual strategies that support informed educational practices and foster more profound understanding. [ Join this Course for Free ](https://www.udemy.com/course/data-driven-visual-communication-visualizationin-in-an-educators-life/?couponCode=ANALYTICSN02) Use COUPON Code **ANALYTICSN02** to get it 100% Free (Valid until 19th June, 2025). Instructor Dr. Nabeela Sulaiman --- ### [Analysis of Covariance (ANCOVA)](https://analyticsn.com/analysis-of-covariance-ancova/) **Published:** October 20, 2024 **Author:** AnalyticsN **Content:** # Analysis of Covariance (ANCOVA) ANCOVA, or Analysis of Covariance, is a powerful statistical technique combining the features of ANOVA and regression analysis. While similar to ANOVA, ANCOVA extends the model by accounting for the association between independent variables, allowing for more accurate results. ANCOVA is widely used in businesses to assess variations in customer behavior, such as determining why consumers prefer one brand over another based on factors like price and brand perception. It also helps analyze how small price changes impact consumer buying habits. ANCOVA involves at least one categorical independent variable (called a "factor") and one interval-based independent variable (termed a "covariate"). Covariates help eliminate extraneous variations, leading to more accurate analysis of the relationship between variables. It is particularly effective when the independent variable shows a strong correlation with the dependent variable. **Benefits of the ANCOVA Test** One of the key advantages of ANCOVA is its ability to control covariates statistically. By adjusting for covariates, ANCOVA removes their impact on the dependent variable, allowing for a more precise assessment of the relationship between categorical factors and the outcome. ANCOVA is a widely preferred test among researchers for this reason. Some benefits of ANCOVA include: - **Increased Statistical Power:** ANCOVA improves precision by accounting for variability within groups, leading to more robust results. - **Removal of Confounding Factors:** ANCOVA adjusts for existing differences between groups, making the analysis more unbiased. **Power Considerations in ANCOVA** Incorporating a covariate in an ANOVA test boosts statistical power by explaining additional variance in the dependent variable. By doing so, ANCOVA enhances the explanatory ability of the independent variables. However, adding a covariate that accounts for little variance may reduce power, so careful consideration of covariate selection is essential. Many students turn to **SPSS ANCOVA tutorials** to master these techniques and avoid pitfalls. **Differences Between ANCOVA and ANOVA** While ANCOVA builds on ANOVA, there are key differences between the two: - **ANOVA** compares the means of three or more groups but cannot control for covariates. - **ANCOVA** adds covariates to the model, allowing it to account for continuous variables alongside categorical factors. Students are advised to familiarize themselves with regression analysis, as it is a crucial part of understanding and applying ANCOVA. **Key Assumptions of ANCOVA** When performing ANCOVA, several assumptions must be met: 1. The variance of the analysis must be independent. 2. Variance must be homogeneous within each group if more than one independent variable is present. 3. The data must come from random sampling. 4. A linear relationship must exist between the independent and dependent variables. 5. Independent variables should be drawn from a normal population with a mean of zero. 6. The regression coefficients must be homogeneous across groups of independent variables. **Applications and Purposes of ANCOVA** - **In Experimental Designs:** ANCOVA controls for variables that cannot be randomized but can be measured on an interval scale. - **In Observational Studies:** ANCOVA helps remove the effects of unwanted variables, improving the relationship between categorical independents and interval dependents. - **In Regression Models:** ANCOVA fits regressions when both categorical and interval independents are present. **Steps to Conduct ANCOVA** 1. Perform a regression analysis between the independent and dependent variables. 2. Identify the residual values from the regression. 3. Conduct an ANOVA on the residuals. **ANCOVA in Business Analysis** At AnalyticsN, our experts use ANCOVA to help businesses analyze the impact of factors like in-store promotions on sales revenue. For instance, in this analysis, the dependent variable is sales revenue, while the independent variable could be consumer attitude. Using ANCOVA allows businesses to understand how promotions influence customer behavior and purchasing patterns. --- ### [Correlation Analysis: A Key Statistical Tool for Data Interpretation](https://analyticsn.com/correlation-analysis-a-key-statistical-tool-for-data-interpretation/) **Published:** October 20, 2024 **Author:** AnalyticsN **Content:** # Correlation Analysis: A Key Statistical Tool for Data Interpretation **Introduction to Correlation Analysis** Correlation analysis is an essential statistical method used to evaluate and interpret the relationships between two or more variables. It allows researchers to quantify the degree of association between variables, helping them uncover trends, patterns, and interdependencies within data sets. Whether you’re analyzing market trends, scientific data, or social behavior, understanding correlations provides valuable insights for decision-making and hypothesis testing. This article explores the concept of correlation, the different types of correlation coefficients, and how they are applied in research and data analysis. ### What is Correlation? In statistical terms, **correlation** refers to the relationship between two random variables or data sets. It measures how variables move in relation to one another—whether they increase, decrease, or remain unchanged when compared. This relationship can be either **positive**, **negative**, or **neutral** (no correlation). Researchers, especially in market studies and social sciences, use correlation analysis to analyze quantitative data collected through surveys, live polls, and other data collection methods. By examining how variables interact, researchers can identify underlying patterns and predict future behaviors or outcomes. ### Correlation Coefficient The **correlation coefficient (r)** is a numerical representation of the degree to which two variables are related. It quantifies the strength and direction of a linear relationship between two variables. The coefficient ranges from **-1 to +1**: - **+1** indicates a perfect positive correlation, where one variable increases as the other increases. - **-1** signifies a perfect negative correlation, where one variable decreases as the other increases. - 0 means there is no correlation or relationship between the two variables. Researchers often use correlation coefficients to evaluate how well one variable can predict another. For instance, in market research, correlation can help determine whether increasing marketing efforts (independent variable) results in higher sales (dependent variable). ### Types of Correlation Coefficients 1. **Pearson Correlation Coefficient (r):** - The Pearson correlation measures the **linear** relationship between two continuous variables. It is the most commonly used type of correlation coefficient. - The formula for the Pearson correlation coefficient is: **![Analyticsn Correlation Analysis: A Key Statistical Tool for Data Interpretation Introduction to Correlation AnalysisCorrelation analysis is an essential statistical method used to evaluate and interpret the relationships between two or more variables. It allows researchers to quantify the degree of association between variables, helping them uncover trends, patterns, and interdependencies within data sets. Whether you’re analyzing Correlation Analysis: A Key Statistical Tool for Data Interpretation Introduction to Correlation AnalysisCorrelation analysis is an essential statistical method used to evaluate and interpret the relationships between two or more variables. It allows researchers to quantify the degree of association between variables, helping them uncover trends, patterns, and interdependencies within data sets. Whether you’re analyzing](https://analyticsn.com/wp-content/uploads/2024/10/Pearson-correlation-coefficient-300x86.png "Pearson correlation coefficient - Analyticsn")** Where: - Xi and Yi are the individual sample points for the two variables. - Xˉ and Yˉ are the means of the X and Y variables, respectively. - Pearson’s correlation is suitable when both variables are normally distributed and have a linear relationship. 2. **Spearman’s Rank Correlation Coefficient (ρ or rₛ):** - Spearman’s correlation is a **non-parametric** measure of rank correlation, making it suitable for ordinal data or when the assumptions of Pearson correlation (normality and linearity) are not met. - The formula for Spearman’s rank correlation is: ![Analyticsn Correlation Analysis: A Key Statistical Tool for Data Interpretation Introduction to Correlation AnalysisCorrelation analysis is an essential statistical method used to evaluate and interpret the relationships between two or more variables. It allows researchers to quantify the degree of association between variables, helping them uncover trends, patterns, and interdependencies within data sets. Whether you’re analyzing Correlation Analysis: A Key Statistical Tool for Data Interpretation Introduction to Correlation AnalysisCorrelation analysis is an essential statistical method used to evaluate and interpret the relationships between two or more variables. It allows researchers to quantify the degree of association between variables, helping them uncover trends, patterns, and interdependencies within data sets. Whether you’re analyzing](https://analyticsn.com/wp-content/uploads/2024/10/Spearmans-rank-correlation-300x146.png "Spearmans rank correlation - Analyticsn") Where: - **d***i* is the difference between the ranks of corresponding variables. - ***n*** is the number of observations. - Spearman’s correlation evaluates the monotonic relationship between two variables, making it useful when the relationship is not strictly linear but still shows a pattern. ### Interpreting Correlation Coefficients Interpreting the value of the correlation coefficient is crucial for understanding the strength and direction of the relationship between variables. - **Strong Positive Correlation (+0.5 to +1):** As one variable increases, the other variable also increases. The scatter plot of the data will show an upward-sloping line. For example, the correlation between education level and income might exhibit a strong positive correlation. - **Weak Positive Correlation (0 to +0.5):** The variables are positively correlated, but the relationship is weaker, with points scattered further from the line. - **No Correlation (0):** There is no discernible relationship between the variables. The scatter plot would show a random distribution of points. - **Weak Negative Correlation (0 to -0.5):** As one variable increases, the other decreases slightly. The points on the scatter plot show a loose downward trend. - **Strong Negative Correlation (-0.5 to -1):** A strong inverse relationship exists, meaning as one variable increases, the other decreases significantly. The scatter plot will show a clear downward-sloping line. ### Application of Correlation in Research Correlation analysis is widely used across different fields, including business, economics, psychology, and healthcare. It is a fundamental step in hypothesis testing, allowing researchers to test the relationship between independent and dependent variables. **1. Testing Hypotheses:** Researchers can test whether there is a statistically significant relationship between variables by calculating the correlation coefficient. If the coefficient is significantly different from zero, they can reject the null hypothesis (which states that no relationship exists). **2. Identifying Trends and Patterns:** By exploring correlations, researchers can identify emerging trends and patterns in their data. For example, correlation analysis might reveal a positive relationship between customer satisfaction and brand loyalty, leading companies to invest in customer experience improvements. **3. Predicting Outcomes:** Correlation analysis helps predict how changes in one variable may influence another. For instance, in finance, researchers may explore the correlation between stock prices and interest rates to predict market trends. **4. Guiding Further Research:** Strong correlations can serve as starting points for further investigation. For example, if a study finds a strong negative correlation between sleep quality and work productivity, researchers might conduct more detailed studies to explore the causal relationship. ### SPSS and Correlation Analysis SPSS (Statistical Package for the Social Sciences) is one of the most popular tools for running correlation analyses. It provides researchers with the ability to calculate both Pearson and Spearman correlation coefficients easily and interpret the results effectively. At **AnalyticsN**, we specialize in helping researchers conduct these analyses, offering tailored SPSS support throughout your research journey. Our SPSS experts guide you through data collection, hypothesis testing, and interpretation of results, ensuring you gain valuable insights from your data. Whether you are dealing with complex datasets or simple bivariate data, our team ensures prompt and accurate analysis, enabling you to make data-driven decisions with confidence. --- ### [Elementor #1180](https://analyticsn.com/spss-analysis-help-for-researchers-and-academics/) **Published:** October 20, 2024 **Author:** AnalyticsN **Content:** ## SPSS Assignment Help for Academics If you’re feeling overwhelmed by tight deadlines and struggling to complete your assignments, AnalyticsN is here to help. We offer expert SPSS assignment assistance for busy professionals, enabling you to focus on your study without worrying about the tools. With our guidance, you can concentrate on the findings and conclusion. **What is SPSS?** SPSS stands for Statistical Package for Social Sciences. Initially developed by IBM in 1968, SPSS transforms raw data into valuable information, aiding in decision-making. This statistical software package allows researchers to conduct complex analyses without needing deep expertise in statistics, making it accessible to all. SPSS is widely used across industries and professions, including by market researchers, government agencies, survey companies, educational researchers, and data analysts. Its user-friendly interface makes it an ideal alternative to more complex tools like Excel, offering a simplified approach for data analysis. AnalyticsN provides top-quality SPSS analysis help to ensure professionals get the support they need. **Applications of SPSS for Statistics Assignments** SPSS is a versatile tool with applications in various statistical techniques: - **Linear Regression:** SPSS helps analyze the relationship between dependent and independent variables. - **Bivariate Statistics:** SPSS can perform ANOVA, t-tests, correlation, parametric, and non-parametric tests. - **Descriptive Statistics:** Calculate measures such as mean, median, mode, range, and more with SPSS’s built-in tools. - **Factor Analysis:** Group similar variables together for easier comparison and analysis. At AnalyticsN, our SPSS experts offer comprehensive help with assignments, including clustering, factor analysis, discriminant analysis, and more. **SPSS Tools for Data Analysis** SPSS is useful for a variety of statistical tests, including but not limited to: - Chi-squared test - Correlation test - Factor analysis - Spearman’s rank correlation - Pearson product-moment correlation - Time series analysis - T-test - Regression analysis - Mann-Whitney U test - ANOVA Whether it’s a dissertation, research paper, or simple assignment, AnalyticsN offers in-depth SPSS research help, assisting researchers worldwide. **SPSS Homework Help** Our SPSS homework assistance is designed to help you learn how to use SPSS effectively while resolving complex statistical problems. AnalyticsN provides you with the tools and support you need to succeed in your studies. Whether you’re tackling quizzes, test papers, or practicals, our SPSS experts are here to guide you. **Final Takeaway** At AnalyticsN, we pride ourselves on delivering high-quality SPSS assignment help quickly and reliably. From business planning and forecasting to data warehousing and quality improvement, SPSS plays a crucial role in decision-making. As a trusted provider, AnalyticsN offers the most reliable SPSS homework and assignment help services online. **FAQs** **What topics do your SPSS research help cover?** We cover a wide range of SPSS topics, including multiple regression, power analysis, correlation analysis, ANOVA, ANCOVA, Cox regression, and more. We also assist with dissertations, literature reviews, and thesis development, all while adhering to IRB guidelines. **What procedure do you follow to complete my SPSS analysis?** Our experts follow a structured approach: we first understand your topic, gather relevant data, organize the assignment as per your university's guidelines, cross-check for errors, and run a plagiarism check before submitting the final draft to you. **How do I know your SPSS analysts are qualified?** Our SPSS tutors are highly qualified, holding advanced degrees, including PhDs. They have extensive experience with research and academic challenges, making them well-equipped to assist you. You can also communicate directly with them through our 24/7 live chat. **How can I be sure you’ll meet your guarantees?** We keep our clients informed every step of the way. You'll receive regular updates via email, and once your order is completed, we’ll notify you via the contact information you provide. You can also track your project through our live chat. --- ### [Forecasting Analysis: A Comprehensive Overview](https://analyticsn.com/forecasting-analysis-a-comprehensive-overview/) **Published:** October 20, 2024 **Author:** AnalyticsN **Content:** # Forecasting Analysis: A Comprehensive Overview Forecasting Analysis is an invaluable tool for researchers, data analysts, and business professionals, enabling them to make informed predictions about future events based on historical data. By analyzing past patterns and trends, forecasting can provide critical insights into future outcomes, allowing for better decision-making, strategic planning, and resource allocation. In this article, we delve into the nuances of Forecasting Analysis, exploring its types, applications, and benefits for business and financial planning. ### What is Forecasting? Forecasting is the process of making predictions about future events based on the analysis of past and present data. It identifies trends, patterns, and relationships within the data to make informed estimates about future outcomes. For example, sales forecasting estimates how much a company expects to sell within a particular period, based on past performance and market conditions. Similarly, financial forecasting involves predicting future financial outcomes, helping companies plan for growth, expenses, and investments. **Types of Forecasting:** 1. **Sales Forecasting:** A prediction of future sales volumes, which helps businesses understand market demand, manage inventory, and optimize marketing strategies. 2. **Financial Forecasting:** Estimations of a company's future financial performance, crucial for budgeting, managing cash flow, and securing funding for capital projects. 3. **Demand Forecasting:** Predicting customer demand to ensure that the right products or services are available when needed, avoiding stockouts or overproduction. 4. **Market Forecasting:** Anticipating market trends, customer behaviors, and competitive landscapes to make informed business decisions and stay ahead in the industry. 5. **Operational Forecasting:** Estimating internal business operations, such as staffing requirements, production levels, and resource allocation, to improve efficiency and cut costs. ### Importance of Forecasting in Business Accurate forecasting is critical to business success. Companies that can predict future sales, market trends, and financial outcomes are better equipped to navigate uncertainties, allocate resources wisely, and develop strategies for growth. Key benefits of forecasting include: - **Informed Decision-Making:** Forecasting provides data-driven insights, empowering decision-makers to create actionable business strategies. - **Risk Mitigation:** Forecasting helps businesses identify potential risks and challenges, enabling them to implement risk management strategies proactively. - **Efficient Resource Allocation:** By predicting future needs, companies can allocate their resources (such as staffing, inventory, and finances) more efficiently, preventing wastage and improving productivity. - **Improved Financial Planning:** Financial forecasting helps businesses make informed decisions regarding capital investment, budgeting, and expansion. - **Enhanced Market Competitiveness:** Businesses that leverage forecasting can stay ahead of competitors by anticipating market shifts and adjusting their strategies accordingly. ### Methods of Forecasting Analysis There are several methods of forecasting, each with its own set of tools and techniques. These methods are categorized into three broad types: 1. **Qualitative Techniques:** - These rely on human judgment, expert opinions, and market research to make predictions. They are most commonly used when historical data is limited or unreliable. - **Examples include:** Delphi method, market research, panel consensus, and executive judgment. 2. **Time Series Analysis and Projection:** - These methods use historical data to identify trends, cycles, and patterns that are expected to continue in the future. - **Examples include:** Moving averages, exponential smoothing, and trend projection. 3. **Causal Models:** - These involve identifying relationships between independent variables (predictors) and dependent variables (outcomes). They are often used to assess how changes in one variable impact another. - **Examples include:** Input-output models, economic models, intention-to-buy surveys, and multiple regression models. ### Key Forecasting Techniques 1. **Moving Average and Exponential Smoothing:** These time series techniques are useful for identifying trends by smoothing out short-term fluctuations. Moving averages calculate the average of data points over a specific period, while exponential smoothing assigns exponentially decreasing weights to older data points. 2. **Input-Output Models:** These models review the interdepartmental flow of goods and services, whether within an economy or within a business, to forecast how changes in one area will affect others. 3. **Multiple-Regression Models:** These statistical models evaluate how multiple independent variables influence a single dependent variable. Multiple regression is widely used in economic forecasting, market research, and social sciences. 4. **Intention-to-Buy and Anticipation Surveys:** Surveys that assess consumers' purchasing intentions or opinions on upcoming products can provide valuable qualitative data for predicting future market demand. ### Applications of Forecasting Analysis Forecasting has a wide range of applications in various industries. Some common uses include: - **Business Operations:** Companies use forecasting to predict future sales, optimize supply chains, and ensure the availability of goods and services. - **Financial Planning:** Financial forecasting helps businesses plan for cash flow, investments, and budgeting, providing a clear picture of future financial health. - **Human Resources:** By forecasting workforce needs, businesses can plan for recruitment, training, and staffing requirements. - **Marketing:** Marketing professionals use forecasting to predict customer behavior, improve campaign effectiveness, and adjust pricing strategies based on anticipated market trends. - **Production and Inventory Management:** Forecasting helps ensure that production levels align with customer demand, reducing inventory costs and preventing overproduction. ### Benefits of Forecasting for Businesses 1. **Strategic Planning:** Forecasting helps businesses develop strategies based on informed predictions, ensuring that they remain competitive and aligned with market trends. 2. **Budgeting and Financial Management:** Accurate financial forecasts enable companies to manage their budgets effectively, ensuring they have sufficient capital for investment, growth, and unexpected challenges. 3. **Operational Efficiency:** By predicting future staffing and production needs, businesses can optimize their operations, reducing costs and improving productivity. 4. **Risk Reduction:** Forecasting allows companies to anticipate potential risks and challenges, enabling them to implement risk mitigation strategies early on. 5. **Innovation and Creativity:** Forecasting encourages businesses to stay ahead of trends, fostering innovation and creative problem-solving as they adapt to changing market conditions. ### Forecasting in Practice at AnalyticsN At **AnalyticsN**, we leverage forecasting techniques to help businesses with financial research, operations planning, and market analysis. Our team uses tools like SPSS to analyze historical data and predict future trends, providing businesses with the insights needed to make strategic decisions. Whether it's conducting efficient market research or making data-driven financial forecasts, our forecasting solutions help organizations enhance creativity, improve decision-making, and achieve sustainable growth. --- ### [SmartPLS Experts For Hire](https://analyticsn.com/our-services/smartpls-experts-for-hire/) **Published:** April 28, 2025 **Author:** AnalyticsN **Content:** Are you searching for **experts in SmartPLS** to analyze your data and validate your research model? Look no further! Our team of seasoned data analysts and structural equation modeling (SEM) specialists provides **professional SmartPLS analytics services** to help you achieve accurate, publication-ready results. ## Get Expert SmartPLS Data Analysis Help SmartPLS is a powerful tool for **partial least squares structural equation modeling (PLS-SEM)**, widely used in business, social sciences, and marketing research. However, interpreting complex models, assessing validity, and ensuring robust results require expertise. Here’s why researchers and businesses trust us: ✅ **Experienced SmartPLS Specialists** – Our team has years of hands-on experience with PLS-SEM, ensuring precise model estimation and validation. ✅ **Comprehensive Analysis** – We handle measurement models (reliability, convergent & discriminant validity) and structural models (path coefficients, R², f², Q²). ✅ **Advanced Techniques** – Expertise in **mediation, moderation, multi-group analysis (MGA), and higher-order constructs**. ✅ **Clear Reporting & Visualization** – Get easy-to-understand reports with tables, charts, and actionable insights. ✅ **Support for Academic & Business Research** – Whether you’re preparing a thesis, journal submission, or market research, we deliver results that meet high standards. ## Our SmartPLS Services Include: 🔹 **Model Development & Validation** – Confirmatory factor analysis (CFA), reliability, and validity checks. 🔹 **Path Analysis & Hypothesis Testing** – Assess relationships between latent variables with bootstrapping. 🔹 **Mediation & Moderation Analysis** – Test indirect and conditional effects in your model. 🔹 **Multi-Group Analysis (MGA)** – Compare groups (e.g., gender, regions) to uncover differences. 🔹 **Higher-Order Constructs** – Evaluate complex hierarchical models. 🔹 **PLS-Predict** – Assess the predictive power of your model. 🔹 **Full Report with AMOS/LISREL Compatibility** – Get detailed documentation for your research. ## Who Can Benefit from Our Services? ✔ **PhD Students & Researchers** – Need help with your dissertation or journal article? We ensure your SEM analysis meets academic standards. ✔ **Universities & Research Institutions** – Get expert support for faculty and student projects. ✔ **Business Analysts & Marketers** – Use PLS-SEM for customer behavior modeling, satisfaction studies, and market segmentation. ## Get Started with a SmartPLS Expert Today! Don’t let statistical challenges delay your research. Our **SmartPLS consultants** are ready to help you achieve reliable, interpretable results. “I was stuck with SmartPLS for my thesis. AnalyticsN delivered perfect results within 3 days — highly recommend!” — Ayesha R., PhD Scholar, UAE ## FAQs What other services do you offer besides data analysis? We do provide other services also, like Thesis consulting, Journal article assistance, Statistical analysis, etc. How is Analytics-N service different from others? Our **SPSS help** includes unique content, on-time delivery, 24\*7 experts’ assistance on online live chat platform, and wide range of statistical services. How do I pay for your Service? We divide the service into milestones. You can pay via Payoneer, WISE, or Western Union. Do you help with report writing? Yes! We explain results and can assist in writing findings. Can you deliver urgently? Yes — 24 to 48-hour delivery options available. ## Get a Quote Your Name Your Email Your Phone Number Tell us about your project Preferred Mode of Communication EmailWhatsappIMO ## Distinctions of AnalyticsN PhD professionals 30+ exceptional experts Zero Plagiarism Strategy Entirely original writings Timely Delivery Never missing deadlines Secured Payments Safe SSL encryption No Secret Charges Zero extra expenses ## How It Works? **Step 1:** Contact Us – Reach out with your project details and requirements using the form above. **Step 2:** Get a Quote – We discuss your needs and provide price and timeline. **Step 3:** Report Delivery – Receive detailed reports, visualizations, and actionable insights. --- ### [SPSS Data Analysis for Statistical Research](https://analyticsn.com/spss-data-analysis-for-statistical-research/) **Published:** October 20, 2024 **Author:** AnalyticsN **Content:** # SPSS Data Analysis for Statistical Research SPSS, short for Statistical Package for the Social Sciences, is a powerful tool designed to simplify statistical and analytical processes. Launched by IBM in 1968, it is widely used across industries for data analysis, particularly in social sciences. SPSS software provides user-friendly functionality, making it accessible even for those without extensive statistical knowledge. With compatibility across MAC OS, Windows, and Linux, it’s an essential tool for researchers, students, and analysts alike. At AnalyticsN, we specialize in helping professionals leverage SPSS for comprehensive data analysis across various sectors. Whether for academic research or business insights, SPSS provides robust techniques and tools to process and analyze data efficiently. Its easy-to-navigate interface means you don’t need to be an expert in statistics to use it effectively. **Get started with our Free Data Analysis Courses** 1. [Data Collection, Formats, and Modelling Techniques](https://analyticsn.com/category/courses/data-collection-formats-and-modelling-techniques/) 2. [Hypothesis Testing with Variance and Correlation](https://analyticsn.com/category/courses/hypothesis-testing-with-variance-and-correlation/) 3. [Structural Equation Modeling](https://analyticsn.com/category/courses/structural-equation-modeling/) **The Power of SPSS in Research** SPSS is invaluable for researchers, students, and analysts, offering data-driven insights crucial for strategic decision-making. By employing SPSS analysis, businesses can develop better growth plans and gain a clearer understanding of market trends. SPSS’s features—such as the missing value technique, factor analysis, and analysis of variance (ANOVA)—ensure precise data handling. In addition, the split file function supports comparative studies, while the variable view option allows for customizable data organization by fields like type, values, label, and measures. With its ability to process data from statistical databases, spreadsheets, and packages, SPSS is a go-to for data analysis. **Using SPSS for Business Data Analysis** When it comes to data analysis, SPSS offers businesses the tools to uncover critical insights that lead to informed decision-making. With the ability to pinpoint the root causes of business issues, SPSS helps you develop cost-effective, growth-oriented strategies. AnalyticsN uses SPSS to provide businesses with the clarity they need to improve customer service, streamline operations, and enhance overall management efficiency. **Benefits of SPSS Data Analysis** The benefits of using SPSS for data analysis are extensive, including: - Generating detailed performance reports for your company - Forecasting potential challenges based on historical data - Improving products and services through data-driven insights - Enhancing management decisions through analytical feedback SPSS makes life easier for professionals in various fields, from researchers to business leaders. AnalyticsN helps you unlock these benefits through our SPSS data analysis services, tailored to suit your unique needs. **How can we help?** At AnalyticsN, we provide expert SPSS data analysis services, academic research consulting, and survey assistance. Our team of SPSS experts is here to help you boost your business growth and profitability by delivering precise, data-driven insights. We also offer training for those looking to master SPSS themselves. No matter your SPSS needs, we’re available around the clock to assist you. Contact us today through the details provided on our website. **FAQ’s** **How can I avail your SPSS data analysis services?** To request SPSS data analysis services from AnalyticsN, simply fill out the form on our homepage. Once we review your requirements, you’ll receive a quote. After payment, our experts will immediately begin working on your project. **How do I know your experts are qualified?** Our team consists of professionals with advanced degrees (including PhDs) from renowned institutions, bringing decades of industry experience to every project. **How much do your services cost?** We offer the most competitive prices in the industry, but costs depend on factors such as the deadline, content type, and any additional services required. Your specific project requirements will also influence pricing. **How long does it take to complete an SPSS analysis?** We handle both regular and urgent requests. Depending on the complexity and urgency, your order may take a few hours to several days to complete. However, we guarantee delivery before your specified deadline. **How can I track my order?** You’ll receive regular updates via email or live chat. Once your project is complete, we will notify you through the email address provided on your submission form. **Is my information kept private?** Yes, we ensure 100% confidentiality. Your data is secure with AnalyticsN, and we never share your personal details with third parties. --- ### [Qualitative Analysis: A Comprehensive Guide](https://analyticsn.com/qualitative-analysis-a-comprehensive-guide/) **Published:** October 20, 2024 **Author:** AnalyticsN **Content:** # Qualitative Analysis: A Comprehensive Guide **Introduction to Qualitative Analysis** When conducting research, selecting the appropriate methodology is crucial for planning and executing the study efficiently. Researchers rely on two broad types of analysis: quantitative and qualitative. While quantitative analysis deals with numerical data and statistical methods, qualitative analysis focuses on non-quantifiable data, providing a deep, nuanced understanding of social phenomena, behaviors, and attitudes. This article delves into the qualitative analysis method, widely utilized by researchers to conduct in-depth critical evaluation and gather meaningful insights. ### What is Qualitative Analysis? Qualitative analysis refers to the process of interpreting non-numeric data, which is often subjective and difficult to measure. This type of analysis emphasizes understanding the **"why"** and **"how"** of human behavior, exploring patterns, themes, and meanings that emerge from the data. Qualitative analysis often involves data such as text, video, interviews, observations, and other non-quantifiable sources. Unlike machines or algorithms, which excel at crunching numbers, humans are better equipped to interpret intangible data, as it requires subjective judgment and context. **Key Characteristics of Qualitative Analysis:** - **Subjective Judgment:** The analysis is interpretive, based on the researcher's personal understanding and insight rather than measurable data. - **Non-Quantifiable Data:** Data such as emotions, behaviors, and cultural interactions are difficult to measure using traditional numeric approaches. - **Smaller Sample Size:** Qualitative analysis typically involves smaller, more focused sample sizes, as the goal is to delve deeper into the subject matter rather than generalize findings across a larger population. - **Non-Statistical:** The method does not rely on statistical techniques but instead focuses on thematic analysis and pattern recognition. - **Exploratory Nature:** It is often used in exploratory research to gain insights into new or complex phenomena that quantitative analysis might overlook. ### Common Qualitative Research Methods Researchers often employ different methods to gather and interpret qualitative data. The three most commonly used techniques are **in-depth interviews**, **focus group discussions (FGDs)**, and **observations**. Each method provides unique insights, allowing researchers to tailor their approach based on the research question. 1. **In-Depth Interviews:** - **Overview:** One-to-one interviews are one of the most widely used qualitative methods. They are semi-structured, allowing flexibility in the conversation so that the interviewee can share detailed information based on their experiences. - **Advantages:** These interviews yield rich data, uncover new insights, and allow face-to-face interaction where both cognitive and affective aspects can be captured. The interviewer can also clarify questions in real-time, leading to more accurate responses. - **Application:** In-depth interviews are frequently used in social research, business studies, and healthcare to explore personal experiences, emotions, and decision-making processes. 2. **Focus Group Discussions (FGDs):** - **Overview:** FGDs involve a group of people discussing a specific topic, concept, or product. The discussion allows researchers to capture a wide range of opinions, beliefs, and attitudes in a relatively short amount of time. - **Advantages:** FGDs help in identifying and defining problems, pre-testing ideas, and generating new concepts. They also provide insights into collective behavior and group dynamics that might not be evident in individual interviews. - **Application:** Businesses often use FGDs for product testing and market research, while social scientists use them to study community attitudes, cultural practices, and social issues. 3. **Observation:** - **Overview:** Observation involves watching and recording behaviors and interactions in a natural setting without interference from the researcher. This method provides insights into the actual behavior of individuals rather than relying on self-reported data. - **Advantages:** Observation allows researchers to understand ongoing processes, social interactions, and individual behaviors in real-time. This method is often used when studying environments where direct questioning is not feasible or desirable. - **Application:** Observation is frequently used in ethnographic research, education studies, and organizational behavior analysis. ### Five Major Qualitative Research Approaches Qualitative research methods can be classified into five main groups, each serving a specific purpose: 1. **Phenomenological Research:** This approach explores the lived experiences of individuals, aiming to understand how people make sense of those experiences. 2. **Narrative Research:** Researchers collect and analyze stories to explore how individuals construct their identities and make sense of their world. 3. **Ethnography:** This method involves immersing oneself in a particular community or culture to study its customs, behaviors, and social interactions. 4. **Grounded Theory:** Grounded theory aims to generate new theories based on the data collected, focusing on identifying patterns, relationships, and underlying processes. 5. **Case Study:** A case study is an in-depth analysis of a specific case (an individual, organization, or event), providing comprehensive insights into complex issues. ### Advantages of Qualitative Analysis Qualitative analysis offers numerous advantages, especially in fields where understanding human experience is critical. Key benefits include: 1. **Deep Understanding:** By exploring individuals’ perceptions and experiences, qualitative analysis allows researchers to gain a deeper understanding of complex issues that quantitative methods may overlook. 2. **Contextual Insights:** It provides context to behaviors, emotions, and decisions, making the findings more relevant and applicable to real-world situations. 3. **Creativity and Flexibility:** Since qualitative analysis is not confined to strict numbers and statistical models, researchers have more freedom to explore different themes and perspectives. 4. **Human-Centered Approach:** Qualitative methods prioritize human experience, giving voice to participants and allowing for a more empathetic and comprehensive view of social phenomena. 5. **Tailored Solutions:** The findings from qualitative research can offer highly tailored solutions for specific industries, organizations, or social issues. ### Applications of Qualitative Analysis 1. **Market Research:** Businesses use qualitative methods to understand consumer behavior, preferences, and motivations, providing insights for product development, branding, and marketing strategies. 2. **Social Sciences:** In sociology, anthropology, and psychology, qualitative analysis helps researchers study societal trends, cultural practices, and human interactions. 3. **Healthcare:** Qualitative methods are essential in healthcare research, providing insights into patient experiences, healthcare provider interactions, and health behavior change. 4. **Education:** Educators and administrators use qualitative research to evaluate teaching methods, curriculum development, and student engagement. ### Qualitative Analysis and Thematic Analysis A common technique in qualitative research is **thematic analysis**, which involves identifying recurring themes and patterns within the data. By creating a framework of themes related to the research topic, researchers can systematically analyze and interpret the findings. Thematic analysis is particularly useful when dealing with large volumes of text, such as interview transcripts, to draw meaningful conclusions about the research question. ### How AnalyticsN Can Help with Qualitative Analysis For researchers across various fields, mastering qualitative analysis is crucial for conducting meaningful studies. At **AnalyticsN**, we specialize in helping researchers navigate the complexities of qualitative analysis using tools like SPSS. Our team provides tailored support throughout the research process, ensuring that you gather authentic data, maintain validity, and apply appropriate theories and concepts for in-depth critical evaluation. Whether you're conducting interviews, focus groups, or observational studies, we are here to assist you in achieving your research goals ethically and efficiently. --- ### [Multiple Regression Analysis: A Powerful Tool for Predictive Modeling](https://analyticsn.com/multiple-regression-analysis-a-powerful-tool-for-predictive-modeling/) **Published:** October 20, 2024 **Author:** AnalyticsN **Content:** # Multiple Regression Analysis: A Powerful Tool for Predictive Modeling ### What is Multiple Regression? **Multiple regression analysis** is a robust statistical method used to examine the relationship between a dependent variable (often referred to as the outcome variable) and multiple independent variables, also known as predictors. It helps researchers assess the individual and collective impact of several predictor variables on an outcome while controlling for other variables. In essence, multiple regression answers key questions such as: - How do several independent variables influence a dependent variable? - Which independent variables are the strongest predictors of the outcome? This technique is widely used across various fields like healthcare, economics, social sciences, and business, enabling data analysts and researchers to make informed predictions and decisions. ### Multiple Regression in Action To understand multiple regression in practice, consider a scenario where researchers aim to predict **blood pressure** (the dependent variable) based on several independent variables such as: - Height - Weight - Age - Hours of exercise per week - Gender By applying multiple regression analysis, researchers can explore how each of these factors contributes to variations in blood pressure, and determine the relative importance of each variable. ### The Multiple Regression Formula The basic formula for multiple regression is: Y=B1​X1​ + B2​X2 ​+…+ Bn​Xn​+C Where: - **Y** = Dependent variable (the outcome we are trying to predict) - **X1​,X2​,…,Xn​ =** Independent variables (the predictors) - **B1​,B2​,…,Bn​ =** Coefficients of the independent variables (showing the strength and direction of the relationship) - **C =** Constant term The coefficients indicate how much the dependent variable changes with a one-unit change in the corresponding independent variable, holding all other variables constant. A **positive coefficient** suggests a positive relationship (as one variable increases, the other also increases), while a **negative coefficient** indicates a negative relationship (as one variable increases, the other decreases). ### How SPSS Simplifies Multiple Regression Analysis SPSS (Statistical Package for the Social Sciences) is a user-friendly tool that enables researchers to conduct multiple regression analysis efficiently. By inputting the relevant variables and specifying the model, SPSS helps generate detailed outputs that allow you to assess the strength of relationships and test hypotheses. ### Steps for Running Multiple Regression in SPSS 1. **Data Sorting:** Before running the regression, ensure your data is clean and organized, with clearly defined dependent and independent variables. 2. **Setting up the Regression Model:** In SPSS, navigate to the regression analysis section and input your dependent and independent variables. Select a **confidence interval of 95%** to estimate the strength and significance of your predictors. 3. **Interpreting R-Squared:** The **R-squared value** measures how well the independent variables predict the dependent variable. It is expressed as a percentage (ranging from 0% to 100%) and indicates how much of the variation in the dependent variable is explained by the independent variables. - For example, an **R-squared of 60%** suggests that 60% of the changes in the dependent variable (e.g., blood pressure) can be explained by the independent variables in the model. 4. **Assessing Coefficients:** Review the coefficients to determine which predictors are statistically significant and how they impact the dependent variable. ### Why Use Multiple Regression? Multiple regression analysis offers numerous benefits to researchers: - **Identifies Key Predictors:** Helps pinpoint which variables have the most influence on the outcome. - **Predictive Power:** Allows for more accurate predictions of future outcomes by considering multiple factors. - **Controls for Confounding Variables:** By including several independent variables, researchers can control for potential confounding effects. - **Actionable Insights:** Facilitates decision-making by highlighting the variables that are most important to target or control in various scenarios, from business decisions to medical interventions. ### Example: Predicting Outcomes in Healthcare Consider a study where researchers are analyzing the impact of lifestyle factors on **heart disease risk**. Using multiple regression, they can investigate how independent variables like **diet, exercise, smoking status,** and **family history** interact to affect the risk level (dependent variable). Through this analysis, researchers may find that exercise has a significant negative effect on heart disease risk, while smoking increases the risk substantially. This information allows healthcare providers to offer more personalized advice based on the most influential risk factors. ### The Benefits of Using SPSS for Multiple Regression SPSS makes multiple regression analysis more accessible to researchers by providing: - **User-Friendly Interface:** Easily input data and run complex analyses without advanced programming skills. - **Comprehensive Output:** SPSS generates detailed reports that include R-squared values, coefficients, significance levels, and more. - **Visualization Tools:** Generate graphs and charts that help illustrate relationships between variables, making it easier to present findings. - **Time Efficiency:** SPSS automates the calculations, allowing for quick analysis of large data sets. ### How We Can Assist with SPSS Multiple Regression If you’re new to multiple regression analysis or need help interpreting your SPSS output, our **SPSS experts** are here to assist. We offer comprehensive support, from setting up your regression model to writing up your results. Our team can guide you through the assumptions, equations, and interpretation, ensuring your analysis is accurate and insightful. We provide tailored **research solutions** for academic, business, and professional research, using the latest statistical tools and methodologies. Whether you need help with your academic thesis, business report, or clinical research, we are here to offer quick, accurate, and reliable support. --- ### [Understanding Factor Analysis: Simplifying Complex Data for Research](https://analyticsn.com/understanding-factor-analysis-simplifying-complex-data-for-research/) **Published:** October 20, 2024 **Author:** AnalyticsN **Content:** # Understanding Factor Analysis: Simplifying Complex Data for Research ### What is Factor Analysis? **Factor analysis** is a statistical technique used to understand the underlying relationships between a large number of observed variables. It simplifies research findings by identifying a smaller number of unobserved variables, or **factors**, that explain the patterns in the data. This method is essential for reducing complexity and making sense of data sets with multiple variables. For instance, variations across several observed variables may be explained by a few unobserved variables. This powerful technique enables researchers to draw meaningful conclusions from otherwise overwhelming data. ### Types of Factor Analysis There are two main types of factor analysis: 1. **Exploratory Factor Analysis (EFA):** Used to uncover the underlying structure of a data set, identifying complex relationships and grouping items into unified concepts. 2. **Confirmatory Factor Analysis (CFA):** A more advanced approach that tests specific hypotheses about how variables are related to particular factors. Both types aim to reduce the number of variables while retaining as much of the original data’s variability as possible. ### How Does Factor Analysis Work? Factor analysis works by taking a large set of variables and determining which are related to underlying factors. For example, a group of six observed variables could be reduced to two unobserved factors that explain most of the variability in the data. The process involves several steps: - Choosing an **estimation method**, such as **Principal Axis Factoring** or **Maximum Likelihood**. - Extracting factors based on common variance. - Applying a **rotation method** (e.g., Varimax) to make the factors more interpretable. - Examining the **Rotated Component Matrix**, which shows the factor loadings (the correlation between each variable and the factor). ### Applications of Factor Analysis Factor analysis is widely used across many fields, including: - **Biology** - **Marketing** - **Operational Research** - **Market Research** - **Financial Research** - **Psychometrics** Researchers use it to explore both **observed** and **unobserved factors** within data, enabling them to uncover hidden patterns and reduce data complexity. ### Factor Analysis in SPSS **SPSS** provides a straightforward approach to conducting factor analysis. To begin: 1. Go to the **“Analyze” menu** in SPSS. 2. Select **Factor Analysis** and choose your variables. 3. Decide on a **rotation method** (e.g., Varimax or Oblimin) to make the factors easier to interpret. 4. The **Rotated Component Matrix** will display the factor loadings for each variable, showing how strongly each variable correlates with the underlying factors. SPSS allows researchers to handle both **observed** and **unobserved factors** easily, making it an invaluable tool for conducting comprehensive data analysis. ### Key Considerations for Factor Analysis - **Factor extraction** involves identifying latent factors that underlie observed data. - **Rotation** helps make the results clearer and more interpretable by adjusting the factor structure. - The **significance level** and variance of factors are crucial for determining their impact on dependent variables. ### How Can We Help? At **AnalyticsN**, we specialize in conducting factor analysis using SPSS to help researchers and professionals uncover meaningful insights from complex data sets. Our team of experts provides: - **Statistical consulting** - **Data analysis** - **Research studies** - **Custom solutions** tailored to your needs Whether you are working in marketing, finance, or operational research, we can assist you in simplifying your data and making informed decisions. Contact us today for **24/7 support** and solutions that meet your research goals. --- ### [About us](https://analyticsn.com/about-us/) **Published:** April 3, 2024 **Author:** AnalyticsN **Content:** ## About AnalyticsnTutor ![video-gallery](https://analyticsn.com/wp-content/uploads/elementor/thumbs/video-gallery-qm5sqlf3u1cxdc8ion7e1v6bdtf62tfb8eoe0dlgh8.webp "video-gallery") ## Company Overview AnalyticsN was founded in 2007 and since then been serving with their experience to the hundreds of individuals and businesses. We offer a wide array of services regarding data analysis and help in every aspect of the research report and data-driven decision-making from both the quantitative and qualitative approaches. From the prospectus through the discussion chapter, we prepare you by engaging our services and leveraging our experience. Our expert team offers data analysis services and brings quality results to you driven by the research data. The core vision of AnalyticsN is to get to your goals with 100% satisfaction. Our experts are always available to assist you and take you toward the goal. From selecting the topic for the research to conduct, we help you edit your PowerPoint slides to ensure the highlights of your study are presented. We value the relationships that we build with our clients and keep transparency for high competency and success rates. ## Why AnalyticsN? It may be difficult to find a trustworthy and reliable company on the web. But our AnalyticsN consulting gives you the best data analysis help across the globe. We ensure the quality work is reviewed by our experienced experts. We deliver the work before the deadline ends with quality. We keep the promises what we make to our clients. --- ### [Terms of Service](https://analyticsn.com/terms-of-service/) **Published:** February 9, 2026 **Author:** AnalyticsN **Content:** **Introduction** Welcome to *AnalyticsN.com* (“we”, “our”, “site”). By accessing or using our website, services, tools, or content (collectively, “Services”), you agree to these Terms of Service (“Terms”). If you do not agree, do not use our Services. **Use of Services** You may use our Services for lawful purposes only. You agree not to engage in any activity that interferes with or disrupts the integrity or performance of the Services. We may update, suspend, or discontinue features at any time without notice. **User Accounts** Some features require creating an account. You are responsible for maintaining the confidentiality of your login credentials and for all activity under your account. You must provide accurate information and promptly update changes. **Content Ownership and License** All content and intellectual property on this site, including text, graphics, and code, are owned by *AnalyticsN.com* or its licensors. You may view and download content for personal, non-commercial use only. No other rights are granted without express written permission. **User-Submitted Content** By submitting data or content through the Services, you grant us a worldwide, royalty-free, non-exclusive license to use, reproduce, modify, and display that content to provide and promote the Services. You represent that you have rights to any content you submit and that it does not infringe third-party rights. **Privacy** Your use of the Services is also governed by our Privacy Policy, which explains how we [collect and use data](https://analyticsn.com/1-data-collection-in-todays-world/) and protects your privacy. **Limitation of Liability** The Services are provided “as is” without warranties of any kind. To the fullest extent permitted by law, *AnalyticsN.com* and its affiliates are not liable for direct, indirect, incidental, or consequential damages arising from your use of the Services. **Indemnification** You agree to indemnify and hold harmless *AnalyticsN.com* from any claims, losses, liabilities, and expenses arising out of your breach of these Terms or your use of the Services. **Changes to Terms** We may modify these Terms at any time. Continued use after changes signifies acceptance. The most recent version will always be posted on this page. **Governing Law** These Terms are governed by the laws of the jurisdiction in which *AnalyticsN.com* is based, without regard to conflict of law principles. **Contact** For questions about these Terms, contact support@analyticsn.com. --- ### [Best SPSS Data Analysis Experts](https://analyticsn.com/our-services/best-spss-data-analysis-experts/) **Published:** April 28, 2025 **Author:** AnalyticsN **Content:** Are you searching for **SPSS experts** to analyze your research data? Our professional **SPSS data analysis services** provide reliable, publication-ready results for students, researchers, and businesses. With our team of PhD-level statisticians, you’ll get precise statistical analysis that meets the highest academic and industry standards. ## Why Choose Our SPSS Analysis Services? SPSS (Statistical Package for the Social Sciences) is the gold standard for statistical analysis in research. But proper data analysis requires expertise – our specialists ensure your tests are correctly selected, executed, and interpreted. Here’s what makes us different: ✅ **PhD-Level Statisticians** – Our team has 10+ years experience in SPSS analysis across various fields ✅ **Comprehensive Analysis** – From basic descriptives to advanced multivariate tests ✅ **All SPSS Versions Supported** – We work with SPSS 20 through the latest versions ✅ **Clear Interpretation** – We explain results in plain English with actionable insights ✅ **Fast Turnaround** – Most projects completed within 2-5 business days ✅ **Publication-Ready Output** – APA-style tables, charts, and write-ups included ## Our SPSS Services Include: 🔹 **Descriptive Statistics** (Means, frequencies, cross-tabulations) 🔹 **Comparative Tests** (t-tests, ANOVA, MANOVA, non-parametric tests) 🔹 **Correlation & Regression Analysis** (Pearson, Spearman, linear/logistic regression) 🔹 **Factor Analysis & Reliability Testing** (PCA, EFA, Cronbach’s alpha) 🔹 **Advanced Modeling** (Multilevel modeling, SEM with AMOS) 🔹 **Data Cleaning & Preparation** (Missing data treatment, outlier detection) 🔹 **Custom Tables & Graphs** (Professional visualization of results) ## Who Benefits from Our Services? ✔ **Students** – Get thesis/dissertation help with proper statistical analysis ✔ **Researchers** – Ensure your journal submissions meet statistical requirements ✔ **Business Analysts** – Make data-driven decisions with expert analysis ✔ **Healthcare Professionals** – Analyze clinical trial and patient data accurately ## Frequently Asked Questions (FAQs) ### ❓ What types of data files can you analyze? We accept all common formats including SPSS (.sav), Excel (.xlsx), CSV, and text files. ### ❓ Can you help choose the right statistical tests? Absolutely! We’ll recommend the most appropriate tests for your research questions and data type. ### ❓ How do you ensure the accuracy of analysis? All work undergoes double-checking by senior statisticians before delivery. ### ❓ What if I need revisions? We offer free revisions to ensure the analysis meets your exact requirements. ### ❓ Do you provide help with interpretation? Yes! We include detailed explanations of all results and their implications. ### ❓ Can you format results in APA style? Definitely. All output is formatted to APA (or your preferred) style guidelines. ### ❓ Is my data kept confidential? 100% confidential. We never share your data and can sign NDAs if required. ### ❓ What if I have missing data? We offer various solutions including multiple imputation and other advanced techniques. ### ❓ Can you analyze qualitative data? Yes, we provide content analysis and thematic coding services. ### ❓ Do you offer emergency/rush services? We provide 24-48 hour rush analysis for urgent projects (additional fee applies). ## Get Expert SPSS Help Today! Stop struggling with complex statistics – let our experts handle your SPSS analysis while you focus on your research. ## FAQs What other services do you offer besides data analysis? We do provide other services also, like Thesis consulting, Journal article assistance, Statistical analysis, etc. How is Analytics-N service different from others? Our **SPSS help** includes unique content, on-time delivery, 24\*7 experts’ assistance on online live chat platform, and wide range of statistical services. How do I pay for your Service? We divide the service into milestones. You can pay via Payoneer, WISE, or Western Union. Do you help with report writing? Yes! We explain results and can assist in writing findings. Can you deliver urgently? Yes — 24 to 48-hour delivery options available. ## Get a Quote Your Name Your Email Your Phone Number Tell us about your project Preferred Mode of Communication EmailWhatsappIMO ## Distinctions of AnalyticsN PhD professionals 30+ exceptional experts Zero Plagiarism Strategy Entirely original writings Timely Delivery Never missing deadlines Secured Payments Safe SSL encryption No Secret Charges Zero extra expenses ## How It Works? **Step 1:** Contact Us – Reach out with your project details and requirements using the form above. **Step 2:** Get a Quote – We discuss your needs and provide price and timeline. **Step 3:** Report Delivery – Receive detailed reports, visualizations, and actionable insights. --- ### [AMOS Experts for Data Modeling](https://analyticsn.com/our-services/amos-experts-for-data-modeling/) **Published:** April 28, 2025 **Author:** AnalyticsN **Content:** Are you looking for **experts in AMOS (Analysis of Moment Structures)** to validate your structural equation models (SEM)? Our professional **AMOS analysis services** provide accurate, publication-ready results for researchers, PhD students, and businesses. With our team of experienced statisticians, you’ll get robust data analysis that meets the highest academic and industry standards. ## Why Choose Our AMOS Analytics Services? AMOS is a leading software for **covariance-based SEM (CB-SEM)**, ideal for testing complex theoretical models in psychology, business, social sciences, and healthcare. Properly executing AMOS analyses requires deep expertise—our specialists ensure your model is correctly specified, validated, and interpreted. Here’s why clients trust us: ✅ **PhD-Level Statisticians** – Our team has extensive experience in AMOS, CB-SEM, and advanced statistical modeling. ✅ **Full Model Testing & Validation** – We assess model fit (CFI, RMSEA, χ²), reliability, and validity (convergent & discriminant). ✅ **Hypothesis & Path Analysis** – Test direct and indirect effects with confidence. ✅ **Mediation & Moderation Analysis** – Examine conditional and indirect relationships in your model. ✅ **Multi-Group Analysis (MGA)** – Compare different populations (e.g., gender, age groups, countries). ✅ **Clear, Publication-Ready Reports** – Detailed explanations, tables, and visualizations for journals/theses. ✅ **Fast Turnaround & Affordable Pricing** – Get accurate results without delays. ## Our AMOS Analysis Services Include: 🔹 **Confirmatory Factor Analysis (CFA)** – Validate measurement models. 🔹 **Structural Equation Modeling (SEM)** – Test theoretical frameworks with path analysis. 🔹 **Model Fit Optimization** – Improve CFI, TLI, RMSEA, and SRMR values. 🔹 **Mediation & Moderation Testing** – Analyze “if” and “how” effects occur. 🔹 **Invariance Testing** – Check measurement equivalence across groups. 🔹 **Bootstrapping for Robust Results** – Ensure statistical significance. 🔹 **Customized Reporting** – APA-style tables, diagrams, and interpretations. ## Who Needs Our AMOS Services? ✔ **PhD Candidates & Researchers** – Struggling with your thesis or journal submission? We ensure your SEM analysis is flawless. ✔ **Universities & Research Centers** – Support your faculty and students with expert statistical help. ✔ **Corporate Researchers & Analysts** – Test business models, customer behavior, and employee satisfaction studies. ## Frequently Asked Questions (FAQs) ### ❓ What is AMOS used for? AMOS is a powerful SEM tool for testing complex relationships between observed and latent variables. It’s widely used in psychology, marketing, management, and social sciences. ### ❓ Can you help with model fit issues? Yes! We specialize in troubleshooting poor fit indices (e.g., high RMSEA, low CFI) by refining measurement models and suggesting modifications. ### ❓ Do you provide support for mediation analysis? Absolutely. We test direct, indirect, and moderated mediation effects using bootstrapping for accurate results. ### ❓ How long does an AMOS analysis take? Turnaround time depends on model complexity, but we typically deliver within **3-7 days** (rush options available). ### ❓ What data format do you accept? We work with SPSS (.sav), Excel (.xlsx), and AMOS-specific files (.amw). ### ❓ Can you help interpret AMOS output? Yes! We provide a **detailed report** with explanations of key findings, tables, and path diagrams. ### ❓ Is your service confidential? 100%. Your data and research are kept secure, and we sign NDAs if required. ### ❓ Do you offer revisions? Yes, we include **free minor revisions** to ensure your complete satisfaction. ## Get Expert AMOS Help Today! Don’t let statistical challenges hold back your research. Our **AMOS specialists** are ready to assist you with precise, reliable analysis. --- ### [Expertise](https://analyticsn.com/expertise/) **Published:** March 15, 2024 **Author:** AnalyticsN **Content:** ![Analyticsn Explore the Expertise Analyticsn has Offered so far A passion for advancing in research and data analytics Our comprehensive suite of professional data services caters to a diverse clientele, ranging from business owners to emerging researchers. Data Cleaning & Screening Getting the data cleaned of missing/unnecessary values, cases, or variables in order to get it's Explore the Expertise Analyticsn has Offered so far A passion for advancing in research and data analytics Our comprehensive suite of professional data services caters to a diverse clientele, ranging from business owners to emerging researchers. Data Cleaning & Screening Getting the data cleaned of missing/unnecessary values, cases, or variables in order to get it's Explore the Expertise analyticsn has offered so far](https://analyticsn.com/wp-content/uploads/2024/03/Lovepik_com-401950164-blue-business-background-1-scaled.jpg "Lovepik_com-401950164-blue-business-background-1 - Analyticsn")# Explore the Expertise *Analyticsn* has Offered so far ## A passion for advancing in research and data analytics Our comprehensive suite of professional data services caters to a diverse clientele, ranging from business owners to emerging researchers. ### Data Cleaning & Screening Getting the data cleaned of missing/unnecessary values, cases, or variables in order to get it’s normality. ### Data Manipulation/Management For the transformation, computation, and filtration to get a clear shape of the [data for targeted research](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) questions ### Research Instrumentation To explore, develop, and validate the research instruments i.e. questionnaire, scale, interview, observation form, checklists etc. ### Data Visualization Visualize the [data attributes and findings for a quick understanding](https://analyticsn.com/data-vs-metrics-understanding-the-core-difference/) of the trends, connections, and predictions ### Data Reports Convert the [data analytics](https://analyticsn.com/?p=1512) results and findings into meaningful insights for data-driven decision-making. ### Literature-driven Solutions To answer the questions regarding, what is already known and where the gap exists, to suggest future research needs ### Which data types are currently accepted by *Analyticsn*? - Quantitative data and Metrics - Historical Records - Surveys - Experiments’ results - Time-series ![Analyticsn Explore the Expertise Analyticsn has Offered so far A passion for advancing in research and data analytics Our comprehensive suite of professional data services caters to a diverse clientele, ranging from business owners to emerging researchers. Data Cleaning & Screening Getting the data cleaned of missing/unnecessary values, cases, or variables in order to get it's Explore the Expertise Analyticsn has Offered so far A passion for advancing in research and data analytics Our comprehensive suite of professional data services caters to a diverse clientele, ranging from business owners to emerging researchers. Data Cleaning & Screening Getting the data cleaned of missing/unnecessary values, cases, or variables in order to get it's](https://analyticsn.com/wp-content/uploads/2024/03/data-type.png "data-type - Analyticsn") ![Analyticsn Explore the Expertise Analyticsn has Offered so far A passion for advancing in research and data analytics Our comprehensive suite of professional data services caters to a diverse clientele, ranging from business owners to emerging researchers. Data Cleaning & Screening Getting the data cleaned of missing/unnecessary values, cases, or variables in order to get it's Explore the Expertise Analyticsn has Offered so far A passion for advancing in research and data analytics Our comprehensive suite of professional data services caters to a diverse clientele, ranging from business owners to emerging researchers. Data Cleaning & Screening Getting the data cleaned of missing/unnecessary values, cases, or variables in order to get it's Windows of a building in Nuremberg, Germany](https://analyticsn.com/wp-content/uploads/2024/03/image.webp "image - Analyticsn") ### Analysis by test type offered by *Analyticsn* - Descriptive Tests - Inferential Tests - Exploratory Tests - Confirmatory Tests - Predictive Tests - Structural Equation Modeling (SEM) - Mediation & Moderation - Multi-group analysis - Time-series analysis - much more… *“Études has saved us thousands of hours of work and has unlocked insights we never thought possible.”* Annie Steiner CEO, Greenprint ## Watch, Read, Listen - --- ## [Expertise](https://analyticsn.com/expertise/) ## Join 900+ subscribers Stay in the loop with everything you need to know. Sign up --- ### [Statistical Analysis Services: SPSS, AMOS & SmartPLS – Expert Data Analysis for Research & Business](https://analyticsn.com/our-services/statistical-analysis-services-spss-amos-smartpls-expert-data-analysis-for-research-business/) **Published:** April 28, 2025 **Author:** AnalyticsN **Content:** ## **Professional Statistical Analysis for Accurate, Publication-Ready Results** Looking for **expert statistical analysis services** for **SPSS, AMOS, or SmartPLS**? Our team of PhD-level statisticians provides **reliable, high-quality data analysis** for academic research, business analytics, and market studies. Whether you need **descriptive statistics, structural equation modeling (SEM), or predictive analytics**, we deliver **clear, actionable insights** with fast turnaround times. ### **Why Choose Our Statistical Analysis Services?** ✅ **PhD-Level Experts** – Our statisticians have 10+ years of experience in **SPSS, AMOS, and SmartPLS** ✅ **All Statistical Methods Covered** – From basic tests to advanced SEM and machine learning ✅ **Fast & Accurate Results** – Most projects completed in **3-5 business days** (rush options available) ✅ **Publication-Ready Reporting** – APA-style tables, graphs, and full interpretations ✅ **Confidential & Secure** – Your data is protected with strict confidentiality ✅ **Affordable Pricing** – Competitive rates for students, researchers, and businesses --- ## **Our Statistical Analysis Services** ### **1. SPSS Data Analysis Services** SPSS is the industry standard for statistical analysis in social sciences, healthcare, and business. Our services include: ✔ **Descriptive Statistics** (Means, frequencies, cross-tabulations) ✔ **Comparative Tests** (t-tests, ANOVA, MANOVA, non-parametric tests) ✔ **Regression Analysis** (Linear, logistic, hierarchical regression) ✔ **Factor Analysis & Reliability Testing** (PCA, EFA, Cronbach’s alpha) ✔ **Data Cleaning & Preparation** (Missing data handling, outlier detection) ✔ **Custom Data Visualization** (Professional charts & graphs) 📌 **Ideal for:** Thesis/dissertation analysis, survey data, clinical research, business analytics ### **2. AMOS (SEM) Analysis Services** AMOS specializes in **covariance-based structural equation modeling (CB-SEM)** for testing complex theoretical models. We provide: ✔ **Confirmatory Factor Analysis (CFA)** – Validate measurement models ✔ **Path Analysis & Hypothesis Testing** – Test direct/indirect effects ✔ **Model Fit Optimization** – Improve CFI, RMSEA, TLI, and SRMR ✔ **Mediation & Moderation Analysis** – Examine conditional effects ✔ **Multi-Group Analysis (MGA)** – Compare groups (gender, age, regions) ✔ **Measurement Invariance Testing** – Ensure consistency across samples 📌 **Ideal for:** Psychology, marketing research, organizational behavior studies ### **3. SmartPLS (PLS-SEM) Analysis Services** SmartPLS is ideal for **partial least squares structural equation modeling (PLS-SEM)**, especially for predictive research. We offer: ✔ **Measurement Model Assessment** – Reliability, convergent & discriminant validity ✔ **Structural Model Testing** – Path coefficients, R², f², Q² ✔ **Mediation & Moderation in PLS-SEM** – Advanced conditional process analysis ✔ **Higher-Order Constructs** – Complex hierarchical modeling ✔ **PLS-Predict** – Assess predictive power of your model ✔ **Multi-Group Comparisons** – Test differences between segments 📌 **Ideal for:** Business research, customer satisfaction studies, exploratory SEM --- ## **Who Uses Our Statistical Analysis Services?** 🔹 **PhD Students & Academics** – Get flawless statistical analysis for dissertations & journal submissions 🔹 **Researchers** – Ensure your data meets publication standards (APA, PLOS, Elsevier, etc.) 🔹 **Businesses & Market Researchers** – Make data-driven decisions with expert analytics 🔹 **Healthcare & Clinical Researchers** – Analyze patient data, clinical trials, and surveys 🔹 **Government & NGOs** – Evidence-based policy research with robust statistics --- ## **Frequently Asked Questions (FAQs)** ### ❓ **What file formats do you accept?** We work with **SPSS (.sav), Excel (.xlsx), CSV, and AMOS/SmartPLS project files**. ### ❓ **Can you help choose the right statistical test?** Yes! We guide you in selecting the best tests for your research questions. ### ❓ **How long does analysis take?** Standard turnaround is **3-5 days** (24-48 hour rush service available). ### ❓ **Do you provide interpretation of results?** Absolutely! We include **detailed explanations** of all findings. ### ❓ **Is my data kept confidential?** 100% – We follow strict **data privacy protocols** and can sign NDAs. ### ❓ **Do you offer revisions?** Yes, we include **free minor revisions** to ensure your satisfaction. ### ❓ **Can you format results in APA style?** Yes, all outputs are formatted to **APA, Harvard, or your preferred style**. ### ❓ **What if I have missing data?** We apply advanced techniques like **multiple imputation** for handling missing values. ### ❓ **Do you support complex SEM models?** Yes! We specialize in **multi-group, mediation, and moderation analysis** in AMOS & SmartPLS. --- ## **Get Expert Statistical Analysis Today!** ✅ **Accurate, error-free results** ✅ **Fast turnaround times** ✅ **PhD-level expertise** ✅ **Affordable pricing** 📩 **Contact us now for a free consultation!** --- ### [Our Services](https://analyticsn.com/our-services/) **Published:** April 23, 2024 **Author:** AnalyticsN **Content:** ## Our Services ### Expert SPSS Data Analysis Services to Power Your Research Success! Our team of experienced data analysts specializes in delivering accurate, reliable, and fast statistical analysis using SPSS. Whether it’s for **academic research, business insights, or consulting,** we’re here to help you make sense of your data. Whether you require **SPSS, AMOS or SmartPLS help**, we are committed to providing you with reliable, efficient, and top-quality results. Experience the difference with **Analytics-N** today! **Our Services Include:**- **SPSS Data Analysis:** Get accurate and detailed statistical reports that simplify complex data. - **Factor Analysis & Regression:** Expert analysis to uncover hidden patterns and trends in your data. - **Hypothesis Testing:** Comprehensive testing to validate your research questions with precision. - **Survey Data Analysis:** Turn raw survey responses into actionable insights. - **Custom Reports & Consultation:** Tailored solutions for your specific research or business needs. Expertise – Data cleaning and screening – Data manipulation and management – Research Instruments Exploration and Validation Analysis by Data type – Quantitative and Metrics – Historical records – Survey – Experimental – Time series Analysis by Test type – Descriptive – Inferential – Predictive – Structural Equation Modelling – Mediation – Moderation – Multi-group – Time-series – Data reports – Data visualization – etc. We Support Following Programs SPSS Comprehensive data analysis including data cleaning, descriptive and inferential statistics, regression analysis, hypothesis testing, and visualizations. SPREADSHEETS/Excel Efficient data management, analysis, and visualization using Excel/Spreadsheets.[Contact me on Fiverr](https://www.fiverr.com/s/qDl4oKZ) AMOS Advanced structural equation modeling (SEM), confirmatory factor analysis (CFA), and path analysis for robust data insights. SmartPLS Expert PLS-SEM analysis with Smart PLS, including model assessment, hypothesis testing, and bootstrapping. WarpPLS Comprehensive data analysis including data cleaning, descriptive and inferential statistics, regression analysis, hypothesis testing, and visualizations. EViews Comprehensive data analysis including data cleaning, descriptive and inferential statistics, regression analysis, hypothesis testing, and visualizations. Documents/Word We provide comprehensive reports in Word in the required format. APA,MLA,Chicago Presentations/PowerPoint Get your data presented in beautiful and professional powerpoint presentations. [ Discuss your Project Requirements ](https://analyticsn.com/contact-us/) ## Our Process We follow a rigorous process to ensure high-quality and reliable data analysis. - **Data Collection:** We start by understanding your data and its sources. - **Data Cleaning:** We clean and prepare your data for analysis. - **Analysis:** We apply the most suitable statistical techniques and models. - **Reporting:** We generate detailed reports with clear interpretations and visualizations. - **Support:** We provide continuous support and follow-up consultations. - - - - - 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) ## Robert Analyticsn-Tutor is helping us for the last 5 years and the quality has been superior and their client-service attitudes are tremendous.. 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) ## Thomas Analyticsn-Tutor provided us with the support we needed to keep our business moving forward. They kept on top of our challenging data and provided an outstanding data analysis service. 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) ## William I would highly recommend Analyticsn-Tutor as they were extremely professional and incredibly helpful throughout the entire process of my complex data analysis. 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) ## Leisha I highly recommend Analyticsn-Tutor. We have worked on numerous projects with this team that have helped us optimise our marketing efforts and better understand our customers 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) 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A **Paired Sample Test** is a statistical method used to compare two measurements taken from the same individual or unit. These paired measurements are typically taken at different times or under different conditions, making this test particularly useful for evaluating changes within the same group. For example, it’s often used to compare **pre-test and post-test scores** to measure the effect of an intervention. In **SPSS**, the **Paired Samples T-Test** is the go-to method for comparing the means of two related measurements. Whether you’re analyzing data before and after a treatment or comparing two conditions for the same subject, this test helps identify if there’s a significant difference between the two sets of measurements. ### Interpretation of Paired Sample Test The Paired Sample Test focuses on analyzing the **difference between two variables** for the same subject. Examples include: - **Pre-test vs. post-test** scores from the same individual. - **Cross-over trials**, where the same individuals receive two different treatments in random order. - **Matched samples**, such as participants matched by characteristics like age or gender. The test can also be applied to situations where each data point in one sample is uniquely matched to a point in the other sample. For example, duplicate measurements on biological samples are considered paired data. ### Formula for Paired T-Test The test statistic is calculated as: ![Analyticsn Understanding Paired Sample Test: A Key Tool for Comparative Analysis What is a Paired Sample Test?A Paired Sample Test is a statistical method used to compare two measurements taken from the same individual or unit. These paired measurements are typically taken at different times or under different conditions, making this test particularly useful for evaluating Understanding Paired Sample Test: A Key Tool for Comparative Analysis What is a Paired Sample Test?A Paired Sample Test is a statistical method used to compare two measurements taken from the same individual or unit. These paired measurements are typically taken at different times or under different conditions, making this test particularly useful for evaluating](https://analyticsn.com/wp-content/uploads/2024/10/Formula-for-Paired-T-Test-300x23.png "Formula for Paired T-Test - Analyticsn") Where: - **H₀** is the null hypothesis (i.e., no difference between means). - **SEMd** is the standard error of the mean difference. The **p-value** helps determine whether the observed differences are statistically significant, providing insight into whether to reject or fail to reject the null hypothesis. ### Steps for Interpreting a Paired T-Test 1. **Determine a Confidence Interval** A **confidence interval** provides a range of likely values for the population mean difference. For example, a 95% confidence interval means that if we took 100 random samples, about 95 of them would include the true population mean difference. 2. **Evaluate Statistical Significance** - If the **p-value ≤ α** (where α is the significance level, usually 0.05), the difference between the means is considered **statistically significant**. This means rejecting the null hypothesis in favor of the alternative hypothesis. - If **p-value > α**, the difference is **not statistically significant**, meaning we fail to reject the null hypothesis. 3. **Check for Data Issues** Ensure your data meets the assumptions of the paired T-test before drawing any conclusions. This includes checking for normality and ensuring that the pairs are matched correctly. ### Practical Applications of Paired Sample Test The Paired Sample Test is ideal when dealing with **repeated measures** or situations where individuals serve as their own controls. Common uses include: - **Pre- and post-intervention studies**. - **Cross-over trials**. - Comparing measurements of the same variable under different conditions for the same subject. ### How We Can Help You At **AnalyticsN**, we offer expert assistance with conducting **Paired Sample Tests** and interpreting your results using **SPSS**. Whether you’re working on a dissertation or need help with your research methodology, our team of specialists provides end-to-end support, including: - **Quantitative analysis** - **Methodology development** - **Results interpretation** We’re here to guide you through your analysis and ensure you have the insights you need to make informed decisions. Contact us today for 24/7 assistance and let us help you take your research to the next level. --- ### [Understanding the Two-Sample Test: A Key Tool in Hypothesis Testing](https://analyticsn.com/understanding-the-two-sample-test-a-key-tool-in-hypothesis-testing/) **Published:** October 20, 2024 **Author:** AnalyticsN **Content:** # Understanding the Two-Sample Test: A Key Tool in Hypothesis Testing ### What is a Two-Sample Test? A **two-sample test** is a statistical method used to determine if there is a significant difference between the means of two independent populations. Commonly employed in fields like **Six Sigma** and scientific research, this test helps assess whether a new process or treatment is more effective than the current one. For example, it can be used to evaluate if a new **sales tool** increases sales compared to an existing tool, or whether a new treatment yields better results than a standard one. The test is typically applied when two small samples (n < 30) are collected from different populations, and researchers wish to determine if their means differ significantly. ### Key Requirements for a Two-Sample Test To conduct a two-sample test, certain criteria must be met: - The samples should be randomly selected from **two independent populations**. - The samples must be **independent** of each other (e.g., not paired or matched). - The sample size for each group should be **less than 30**. - The samples should be **normally distributed**. Two key variables are needed: 1. One variable defines the **two groups** being compared. 2. The other variable measures the **outcome of interest** for each group. ### Types of Two-Sample Tests There are two main types of two-sample tests, depending on whether the variances of the two populations are assumed to be equal or unequal. 1. **Two-Sample T-Test (Equal Variance)**: Assumes that the variances of both populations are the same. 2. **Two-Sample T-Test (Unequal Variance)**: Assumes that the variances of the two populations are not equal. ### Formulas for Two-Sample T-Tests **For Equal Variance**: ![](https://analyticsn.com/wp-content/uploads/2024/10/Equal-Variance-300x17.png) Where: - ***n1*** and ***n2*** are the sample sizes, - **x̅\_1** and **x̅\_2** are the sample means, and - **S***p* is the pooled standard deviation. **For Unequal Variance**: ![](https://analyticsn.com/wp-content/uploads/2024/10/Unequal-Variance-300x16.png) Where: - ![Analyticsn Understanding the Two-Sample Test: A Key Tool in Hypothesis Testing What is a Two-Sample Test?A two-sample test is a statistical method used to determine if there is a significant difference between the means of two independent populations. Commonly employed in fields like Six Sigma and scientific research, this test helps assess whether a new process Understanding the Two-Sample Test: A Key Tool in Hypothesis Testing What is a Two-Sample Test?A two-sample test is a statistical method used to determine if there is a significant difference between the means of two independent populations. Commonly employed in fields like Six Sigma and scientific research, this test helps assess whether a new process](https://analyticsn.com/wp-content/uploads/2024/10/s1-and-s2.png "s1 and s2 - Analyticsn") are the variances of samples 1 and 2. ### Questions Addressed by Two-Sample T-Tests Two-sample t-tests are typically used to answer key questions, such as: - Is **process 1** equivalent to **process 2**? - Does the **new process** perform better than the **current one**? - Is the difference between the two processes **statistically significant**? A pre-determined **threshold** is often set to compare the performance of the new process against the current process. ### Advantages of the Two-Sample Test The two-sample test offers several benefits: 1. **Independence of Groups:** It allows researchers to compare the means of two groups that are unrelated, which is particularly useful when the observations from one group have no influence on those of the other. 2. **Accuracy in Inference:** The test helps researchers determine whether the observed differences between two groups are statistically significant, accounting for factors like the degree of freedom and standard error. 3. **Broad Applications:** This test is widely used across industries to evaluate changes, improvements, or differences between two populations, making it an essential tool for decision-making. ### Objectives of the Two-Sample Test The main objective of a two-sample test is to assess whether the difference between the two populations is **statistically significant**. It is used to evaluate: - Whether the means of two independent groups differ, - How significant the difference is in the context of the summary statistics, and - If the difference is meaningful enough to reject the **null hypothesis** (which suggests no difference between the two populations). ### Differences Between One-Sample and Two-Sample Tests While the **one-sample t-test** compares the mean of a single group to a known value, the **two-sample t-test** compares the means of two different groups. Additionally, a **paired t-test** is used when comparing two groups that are related (e.g., before and after treatment in the same group). ### Applications of the Two-Sample Test The two-sample test is applied in various situations to test an alternative hypothesis about the population means and variances. 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[Metrics analysis](https://analyticsn.com/tag/metrics-analysis/) --- ### [Business performance](https://analyticsn.com/tag/business-performance/) --- ### [Performance tracking](https://analyticsn.com/tag/performance-tracking/) --- ### [Business success](https://analyticsn.com/tag/business-success/) --- ### [Strategic decisions](https://analyticsn.com/tag/strategic-decisions/) --- ### [Raw data](https://analyticsn.com/tag/raw-data/) --- ### [Actionable insights](https://analyticsn.com/tag/actionable-insights/) --- ### [Quantifiable measurements](https://analyticsn.com/tag/quantifiable-measurements/) --- ### [Information processing](https://analyticsn.com/tag/information-processing/) --- ### [Data vs Metrics comparison.](https://analyticsn.com/tag/data-vs-metrics-comparison/) --- ### [ddata](https://analyticsn.com/tag/ddata/) --- ### [metrics](https://analyticsn.com/tag/metrics/) --- ### [Business dashboards](https://analyticsn.com/tag/business-dashboards/) --- ### [Custom dashboards](https://analyticsn.com/tag/custom-dashboards/) --- ### [Tableau](https://analyticsn.com/tag/tableau/) --- ### [Stakeholder reporting](https://analyticsn.com/tag/stakeholder-reporting/) --- ### [Interactive dashboards](https://analyticsn.com/tag/interactive-dashboards/) --- ### [Data storytelling](https://analyticsn.com/tag/data-storytelling/) --- ### [Data insights](https://analyticsn.com/tag/data-insights/) --- ### [Data centralization](https://analyticsn.com/tag/data-centralization/) --- ### [Dashboards](https://analyticsn.com/tag/dashboards/) --- ### [Data trends](https://analyticsn.com/tag/data-trends/) --- ### [Real-time data](https://analyticsn.com/tag/real-time-data/) --- ### [Visualization tools](https://analyticsn.com/tag/visualization-tools/) --- ### [Tableau dashboards](https://analyticsn.com/tag/tableau-dashboards/) --- ### [Dashboard examples](https://analyticsn.com/tag/dashboard-examples/) --- ### [Dashboard 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[secondary data analysis](https://analyticsn.com/tag/secondary-data-analysis/) --- ### [statistical methods](https://analyticsn.com/tag/statistical-methods/) --- ### [inferences](https://analyticsn.com/tag/inferences/) --- ### [ANOVA](https://analyticsn.com/tag/anova/) --- ### [sampling variability](https://analyticsn.com/tag/sampling-variability/) --- ### [Central Limit Theorem](https://analyticsn.com/tag/central-limit-theorem/) --- ### [sample and population](https://analyticsn.com/tag/sample-and-population/) --- ### [population parameters](https://analyticsn.com/tag/population-parameters/) --- ### [sample statistics](https://analyticsn.com/tag/sample-statistics/) --- ### [confidence intervals](https://analyticsn.com/tag/confidence-intervals/) --- ### [representative sample](https://analyticsn.com/tag/representative-sample/) --- ### [inferential statistics](https://analyticsn.com/tag/inferential-statistics/) --- ### [normal distribution](https://analyticsn.com/tag/normal-distribution/) --- ### [Analysis of Variance (ANOVA)](https://analyticsn.com/tag/analysis-of-variance-anova/) --- ### [Chi-Square Test](https://analyticsn.com/tag/chi-square-test/) --- ### [statistical tests](https://analyticsn.com/tag/statistical-tests/) --- ### [significance level](https://analyticsn.com/tag/significance-level/) --- ### [alternative hypothesis](https://analyticsn.com/tag/alternative-hypothesis/) --- ### [null hypothesis](https://analyticsn.com/tag/null-hypothesis/) --- ### [p-value](https://analyticsn.com/tag/p-value/) --- ### [Type I Error](https://analyticsn.com/tag/type-i-error/) --- ### [Null hypothesis (H0)](https://analyticsn.com/tag/null-hypothesis-h0/) --- ### [Alternative hypothesis (Ha)](https://analyticsn.com/tag/alternative-hypothesis-ha/) --- ### [Depression](https://analyticsn.com/tag/depression/) --- ### [Young adults](https://analyticsn.com/tag/young-adults/) --- ### [Sample size](https://analyticsn.com/tag/sample-size/) --- ### [Inferential tests](https://analyticsn.com/tag/inferential-tests/) --- ### [Confidence level](https://analyticsn.com/tag/confidence-level/) --- ### [Smoking](https://analyticsn.com/tag/smoking/) --- ### [Statistical significance](https://analyticsn.com/tag/statistical-significance/) --- ### [Significance level (α)](https://analyticsn.com/tag/significance-level-α/) --- ### [Research findings](https://analyticsn.com/tag/research-findings/) --- ### [Cigarettes](https://analyticsn.com/tag/cigarettes/) --- ### [Association](https://analyticsn.com/tag/association/) --- ### [gender](https://analyticsn.com/tag/gender/) --- ### [exam scores](https://analyticsn.com/tag/exam-scores/) --- ### [Chi-Square Test of Independence](https://analyticsn.com/tag/chi-square-test-of-independence/) --- ### [bivariate](https://analyticsn.com/tag/bivariate/) --- ### [correlation coefficient](https://analyticsn.com/tag/correlation-coefficient/) --- ### [voting preference](https://analyticsn.com/tag/voting-preference/) --- ### [weight loss](https://analyticsn.com/tag/weight-loss/) --- ### [categorical](https://analyticsn.com/tag/categorical/) --- ### [response variable](https://analyticsn.com/tag/response-variable/) --- ### [explanatory variable](https://analyticsn.com/tag/explanatory-variable/) --- ### [smoking status](https://analyticsn.com/tag/smoking-status/) --- ### [p-values](https://analyticsn.com/tag/p-values/) --- ### [quantitative](https://analyticsn.com/tag/quantitative/) --- ### [exercise regimens](https://analyticsn.com/tag/exercise-regimens/) --- ### [income](https://analyticsn.com/tag/income/) --- ### [statistical tools](https://analyticsn.com/tag/statistical-tools/) --- ### [study hours](https://analyticsn.com/tag/study-hours/) --- ### [statistical evidence](https://analyticsn.com/tag/statistical-evidence/) --- ### [research](https://analyticsn.com/tag/research/) --- ### [statistical testing](https://analyticsn.com/tag/statistical-testing/) --- ### [boxplot](https://analyticsn.com/tag/boxplot/) --- ### [means comparison](https://analyticsn.com/tag/means-comparison/) --- ### [dataset](https://analyticsn.com/tag/dataset/) --- ### [sample data](https://analyticsn.com/tag/sample-data/) --- ### [F-test](https://analyticsn.com/tag/f-test/) --- ### [variation among groups](https://analyticsn.com/tag/variation-among-groups/) --- ### [SAS](https://analyticsn.com/tag/sas/) --- ### [variation within groups](https://analyticsn.com/tag/variation-within-groups/) --- ### [academic frustration](https://analyticsn.com/tag/academic-frustration/) --- ### [variation](https://analyticsn.com/tag/variation/) --- ### [college major](https://analyticsn.com/tag/college-major/) --- ### [post hoc tests](https://analyticsn.com/tag/post-hoc-tests/) --- ### [Tukey’s Honestly Significant Difference](https://analyticsn.com/tag/tukeys-honestly-significant-difference/) --- ### [family-wise error rate](https://analyticsn.com/tag/family-wise-error-rate/) --- ### [Bonferroni Procedure](https://analyticsn.com/tag/bonferroni-procedure/) --- ### [paired comparisons](https://analyticsn.com/tag/paired-comparisons/) --- ### [Scheffé Test](https://analyticsn.com/tag/scheffe-test/) --- ### [Dunnett’s Multiple Comparison Test](https://analyticsn.com/tag/dunnetts-multiple-comparison-test/) --- ### [Sidak Test](https://analyticsn.com/tag/sidak-test/) --- ### [ethnicity](https://analyticsn.com/tag/ethnicity/) --- ### [cigarettes smoked](https://analyticsn.com/tag/cigarettes-smoked/) --- ### [Holm Test](https://analyticsn.com/tag/holm-test/) --- ### [Duncan Multiple Range Test](https://analyticsn.com/tag/duncan-multiple-range-test/) --- ### [Fisher’s Least Significant Difference](https://analyticsn.com/tag/fishers-least-significant-difference/) --- ### [Duncan Post Hoc Results](https://analyticsn.com/tag/duncan-post-hoc-results/) --- ### [significant 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relationship](https://analyticsn.com/tag/strength-of-relationship/) --- ### [Industries collect data](https://analyticsn.com/tag/industries-collect-data/) --- ### [Data collection](https://analyticsn.com/tag/data-collection/) --- ### [Annual business survey](https://analyticsn.com/tag/annual-business-survey/) --- ### [Social media data](https://analyticsn.com/tag/social-media-data/) --- ### [Ethics in data collection](https://analyticsn.com/tag/ethics-in-data-collection/) --- ### [Patient survey data](https://analyticsn.com/tag/patient-survey-data/) --- ### [United States Census Bureau data](https://analyticsn.com/tag/united-states-census-bureau-data/) --- ### [Online searches](https://analyticsn.com/tag/online-searches/) --- ### [Cookies in data collection](https://analyticsn.com/tag/cookies-in-data-collection/) --- ### [Advertisers and online habits](https://analyticsn.com/tag/advertisers-and-online-habits/) --- ### [Microscope data collection](https://analyticsn.com/tag/microscope-data-collection/) --- ### [Observations in data collection](https://analyticsn.com/tag/observations-in-data-collection/) --- ### [questionnaires](https://analyticsn.com/tag/questionnaires/) --- ### [Mobile devices data](https://analyticsn.com/tag/mobile-devices-data/) --- ### [Healthcare industry data](https://analyticsn.com/tag/healthcare-industry-data/) --- ### [Scientific data generation](https://analyticsn.com/tag/scientific-data-generation/) --- ### [Privacy in data collection](https://analyticsn.com/tag/privacy-in-data-collection/) --- ### [Forms](https://analyticsn.com/tag/forms/) --- ### [User information storage](https://analyticsn.com/tag/user-information-storage/) --- ### [and surveys](https://analyticsn.com/tag/and-surveys/) --- ### [Telemedicine vs. in-person doctor visits](https://analyticsn.com/tag/telemedicine-vs-in-person-doctor-visits/) --- ### [Job interview data collection](https://analyticsn.com/tag/job-interview-data-collection/) --- ### [Online ads and user preferences](https://analyticsn.com/tag/online-ads-and-user-preferences/) --- ### [Digital photo data](https://analyticsn.com/tag/digital-photo-data/) --- ### [Animal behavior study data](https://analyticsn.com/tag/animal-behavior-study-data/) --- ### [Data analysis process](https://analyticsn.com/tag/data-analysis-process/) --- ### [Data type selection](https://analyticsn.com/tag/data-type-selection/) --- ### [Data trustworthiness](https://analyticsn.com/tag/data-trustworthiness/) --- ### [Business problem-solving with data](https://analyticsn.com/tag/business-problem-solving-with-data/) --- ### [Data timeframe](https://analyticsn.com/tag/data-timeframe/) --- ### [Random sample](https://analyticsn.com/tag/random-sample/) --- ### [High-volume traffic times](https://analyticsn.com/tag/high-volume-traffic-times/) --- ### [Data approval](https://analyticsn.com/tag/data-approval/) --- ### [Data accuracy](https://analyticsn.com/tag/data-accuracy/) --- ### [Third-party data](https://analyticsn.com/tag/third-party-data/) --- ### [Traffic pattern analysis](https://analyticsn.com/tag/traffic-pattern-analysis/) --- ### [Strategic data collection](https://analyticsn.com/tag/strategic-data-collection/) --- ### [Data bias](https://analyticsn.com/tag/data-bias/) --- ### [Analyzing datasets](https://analyticsn.com/tag/analyzing-datasets/) --- ### [Data reliability](https://analyticsn.com/tag/data-reliability/) --- ### [Population in data analytics](https://analyticsn.com/tag/population-in-data-analytics/) --- ### [Second-party data](https://analyticsn.com/tag/second-party-data/) --- ### [Time series data](https://analyticsn.com/tag/time-series-data/) --- ### [Data credibility](https://analyticsn.com/tag/data-credibility/) --- ### [Traffic data](https://analyticsn.com/tag/traffic-data/) --- ### [Data sources](https://analyticsn.com/tag/data-sources/) --- ### [Data sample size](https://analyticsn.com/tag/data-sample-size/) --- ### [First-party data](https://analyticsn.com/tag/first-party-data/) --- ### [Trends over time](https://analyticsn.com/tag/trends-over-time/) --- ### [Data format](https://analyticsn.com/tag/data-format/) --- ### [Movie genres](https://analyticsn.com/tag/movie-genres/) --- ### [Data classification](https://analyticsn.com/tag/data-classification/) --- ### [Continuous data](https://analyticsn.com/tag/continuous-data/) --- ### [Primary data](https://analyticsn.com/tag/primary-data/) --- ### [External data](https://analyticsn.com/tag/external-data/) --- ### [Unstructured data](https://analyticsn.com/tag/unstructured-data/) --- ### [Data measurement](https://analyticsn.com/tag/data-measurement/) --- ### [Movie data](https://analyticsn.com/tag/movie-data/) --- ### [Internal data](https://analyticsn.com/tag/internal-data/) --- ### [Box office revenue](https://analyticsn.com/tag/box-office-revenue/) --- ### [Structured data](https://analyticsn.com/tag/structured-data/) --- ### [Movie ratings](https://analyticsn.com/tag/movie-ratings/) --- ### [Credit reports](https://analyticsn.com/tag/credit-reports/) --- ### [National average wages](https://analyticsn.com/tag/national-average-wages/) --- ### [HR data](https://analyticsn.com/tag/hr-data/) --- ### [Secondary data](https://analyticsn.com/tag/secondary-data/) --- ### [Demographic data](https://analyticsn.com/tag/demographic-data/) --- ### [Sales data](https://analyticsn.com/tag/sales-data/) --- ### [Ordinal data](https://analyticsn.com/tag/ordinal-data/) --- ### [Budget data](https://analyticsn.com/tag/budget-data/) --- ### [Discrete data](https://analyticsn.com/tag/discrete-data/) --- ### [Product inventory](https://analyticsn.com/tag/product-inventory/) --- ### [Customer profiles](https://analyticsn.com/tag/customer-profiles/) --- ### [Questionnaire data](https://analyticsn.com/tag/questionnaire-data/) --- ### [Census 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attributes](https://analyticsn.com/tag/data-attributes/) --- ### [Number data type](https://analyticsn.com/tag/number-data-type/) --- ### [String data type](https://analyticsn.com/tag/string-data-type/) --- ### [Boolean data type](https://analyticsn.com/tag/boolean-data-type/) --- ### [Data formatting in spreadsheets](https://analyticsn.com/tag/data-formatting-in-spreadsheets/) --- ### [Boolean logic](https://analyticsn.com/tag/boolean-logic/) --- ### [Boolean operators](https://analyticsn.com/tag/boolean-operators/) --- ### [Truth table](https://analyticsn.com/tag/truth-table/) --- ### [Data filtering](https://analyticsn.com/tag/data-filtering/) --- ### [Boolean conditions](https://analyticsn.com/tag/boolean-conditions/) --- ### [Logical operators](https://analyticsn.com/tag/logical-operators/) --- ### [Boolean statements](https://analyticsn.com/tag/boolean-statements/) --- ### [Boolean logic in queries](https://analyticsn.com/tag/boolean-logic-in-queries/) --- ### [AND OR 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