Part XIII — Regression and Statistical Models¶
Fitting and criticizing models that predict one variable from others: linear regression and its generalizations, regularization, and the diagnostic tools that reveal when a fitted model is lying.
Topics¶
| Topic | Focus |
|---|---|
| Simple Linear Regression | One-predictor least squares and its assumptions |
| Multiple Linear Regression | Least squares with many predictors |
| Generalized Linear Models | Link functions and exponential-family responses |
| Logistic Regression | Regression for binary outcomes |
| Regularization | Ridge and lasso penalties against overfitting |
| Model Diagnostics | Checking fit, influence, and assumption violations |
| Residual Analysis | What residual patterns reveal about a model |