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Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

- Emphasis on practice rather than theory sets this apart from other texts

- Three chapters on causal inference

- Code and data for all examples in the book are available on the web site in the popular open-source programs R and Stan

The authors are experienced researchers who have published articles in hundreds of different scientific journals in fields including statistics, computer science, policy, public health, political science, economics, sociology, and engineering. They have also published articles in the Washington Post, New York Times, Slate, and other public venues. Their previous books include Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, and Data Analysis and Regression Using Multilevel/Hierarchical Models.

Andrew Gelman is Higgins Professor of Statistics and Professor of Political Science at Columbia University.

Jennifer Hill is Professor of Applied Statistics at New York University.

Aki Vehtari is Associate Professor in Computational Probabilistic Modeling at Aalto University, Finland.

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
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