Regression and Other Stories

My notes on Regression and Other Stories by Andrew Gelman et al.

These are my notes for the book “Regression and Other Stories” by Gelman, Hill, and Vehtari (2021) . This notebook contains summaries, code examples, and reflections on the material presented in the book. I have recreated all the code using Python.

Part 1: Fundamentals

1. Overview

2. Data and measurement

3. Some basic methods in mathematics and probability

4. Statistical inference

5. Simulation

Part 2: Linear regression

6. Background on regression modeling

7. Linear regression with a single predictor

8. Fitting regression models

9. Prediction and Bayesian inference

10. Linear regression with multiple predictors

11. Assumptions, diagnostics, and model evaluation

12. Transformations and regression

Part 3: Generalized linear models

13. Logistic regression

14. Working with logistic regression

15. Other generalized linear models

Part 4: Before and after fitting a regression

16. Design and sample size decisions

17. Poststratification and missing-data imputation

Part 5: Causal inference

18. Causal inference and randomized experiments

19. Causal inference using regression on the treatment variable

20. Observational studies with all confounders assumed to be measured

21. Additional topics in causal inference

Part 6: What comes next?

22. Advanced regression and multilevel models