Bivariate & Multivariate Exploration
Bivariate & Multivariate Exploration groups the core ideas a learner needs at the 3 · data understanding & eda stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Scatterplots and associationScatterplots and association is a communication and diagnostic technique that maps data or results into a visual form. A useful visual makes comparisons easy, exposes uncertainty and supports the analytical question rather than merely decorating the report.
02Cross-tabulation and contingency tablesCross-tabulation and contingency tables is a communication and diagnostic technique that maps data or results into a visual form. A useful visual makes comparisons easy, exposes uncertainty and supports the analytical question rather than merely decorating the report.
03Grouped summariesGrouped summaries is a practical concept within Bivariate & Multivariate Exploration. It helps turn the broader workflow stage “3 · Data Understanding & EDA” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04Correlation matricesCorrelation matrices is a practical concept within Bivariate & Multivariate Exploration. It helps turn the broader workflow stage “3 · Data Understanding & EDA” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Pair plots and multivariate patternsPair plots and multivariate patterns is a communication and diagnostic technique that maps data or results into a visual form. A useful visual makes comparisons easy, exposes uncertainty and supports the analytical question rather than merely decorating the report.
06Confounding and Simpson’s paradoxConfounding and Simpson’s paradox is a practical concept within Bivariate & Multivariate Exploration. It helps turn the broader workflow stage “3 · Data Understanding & EDA” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.