▶Book Description
Associations and correlations are ways of describing how a pair of variables change together as a result of their connection. By knowing the various available techniques, you can easily and accurately discover and visualize the relationships in your data.
This book begins by showing you how to classify your data into the four distinct types that you are likely to have in your dataset. Then, with easy-to-understand examples, you'll learn when to use the various univariate and multivariate statistical tests. You'll also discover what to do when your univariate and multivariate results do not match. As the book progresses, it describes why univariate and multivariate techniques should be used as a tag team, and also introduces you to the techniques of visualizing the story of your data.
By the end of the book, you'll know exactly how to select the most appropriate univariate and multivariate tests, and be able to use a single strategic framework to discover the true story of your data.
▶What You Will Learn
- Identify a dataset that's fit for analysis using its basic features
- Understand the importance of associations and correlations
- Use multivariate and univariate statistical tests to confirm relationships
- Classify data as qualitative or quantitative and then into the four subtypes
- Build a visual representation of all the relationships in the dataset
- Automate associations and correlations with CorrelViz
▶Key Features
- Get a comprehensive introduction to associations and correlations
- Explore multivariate analysis, understand its limitations, and discover the assumptions on which it's based
- Gain insights into the various ways of preparing your data for analysis and visualization
▶Who This Book Is For
This is a book for beginners – if you're a novice data analyst or data scientist, then this is a great place to start. Experienced data analysts might also find value in this title, as it will recap the basics and strengthen your understanding of key concepts. This book focuses on introducing the essential elements of association and correlation analysis.
▶What this book covers
- Chapter 1, Data Collection and Cleaning, briefly (very briefly) introduces data collection and cleaning, and outlines the basic features of a dataset that is fit for purpose and ready for analysis.
- Chapter 2, Data Classification, discusses how to classify your data and introduces the four distinct types of data that you'll likely have in your dataset.
- Chapter 3, Introduction to Associations and Correlations, introduces associations and correlations, explains what they are, and their importance in understanding the world around us.
- Chapter 4, Univariate Statistics, discusses the univariate statistical tests that are common in association and correlation analysis, and details how and when to use them, with simple easy-to-understand examples.
- Chapter 5, Multivariate Statistics, introduces the different types of multivariate statistics, and how and when to use them. This chapter includes a discussion of confounding, suppressor, and interacting variables, and what to do when your univariate and multivariate results do not concur (spoiler alert: the answer is not panic!).
- Chapter 6, Vizualising Your Relationships, explains the holistic strategy of discovering all the independent relationships in your dataset and describes why univariate and multivariate techniques should be used as a tag team. This chapter also introduces you to the techniques of visualizing the story of your data.
- Chapter 7, Bonus: Automating Associations and Correlations, is a bonus chapter that explains how you can discover all the associations and correlations in your data automatically, and in minutes rather than months.
