A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.
Information zum Autor
Columbia University, New York
Preface; Part 0. Fundamentals: 1. Overview; 2. Data and measurement; 3. Some basic methods in mathematics and probability; 4. Generative models and statistical inference; 5. Simulation; Part I. 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 II. Generalized linear models: 13. Logistic regression; 14. Working with logistic regression; 15. Other generalized linear models; Part III. Before and after fitting a regression: 16. Design and sample size decisions; 17. Poststratification and missing-data imputation; Part IV. 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. More advanced topics in causal inference; Part V. What comes next?: 22. Advanced regression and multilevel models; Appendices: A. Six quick tips to improve your regression modeling; B. Computing in R; References; Author index; Subject index.