Linear and logistic regression
Regression describes relationships between predictors and outcomes under assumptions that must be checked. The choice of model depends chiefly on the nature of the outcome.
Open interactive lesson & self-check ↗Linear and logistic regression — learning map
Original conceptual SVG · scalableOriginal schematic relationship map for this topic; relationships are organized for study, not intended as an anatomical depiction or a diagnostic algorithm.
Linear regression
A typical linear model estimates the conditional mean of a continuous response as a function of predictors and an error term.
Logistic regression
A logistic model relates predictors to the log-odds of a binary event and can produce adjusted odds ratios.
Confounding and selection
Including a variable in a model does not automatically remove confounding; causal structure, measurement quality and selection affect interpretation.
Model checks
Inspect residual patterns where relevant, linearity assumptions, influential observations, calibration, missingness and uncertainty in estimates.
Reference and next reading
Explore the original curriculum and publisher-hosted resources for full-depth reading; this note is an original schematic introduction, not an exhaustive chapter.