Regression and Other Stories
Regression and Other Stories
Jennifer Hill, New York University
Aki Vehtari, Aalto University, Finland
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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
Reviews & endorsements
'Gelman, Hill and Vehtari provide an introductory regression book that hits an amazing trifecta: it motivates regression using real data examples, provides the necessary (but not superfluous) theory, and gives readers tools to implement these methods in their own work. The scope is ambitious - including introductions to causal inference and measurement - and the result is a book that I not only look forward to teaching from, but also keeping around as a reference for my own work.' Elizabeth Tipton, Northwestern University
'Regression and Other Stories is simply the best introduction to applied statistics out there. Filled with compelling real-world examples, intuitive explanations, and practical advice, the authors offer a delightfully modern perspective on the subject. It’s an essential resource for students and practitioners across the statistical and social sciences.' Sharad Goel, Department of Management Science and Engineering, Stanford University
'With modern software it is very easy to fit complex regression models, and even easier to get their interpretation completely wrong. This wonderful book, summarising the authors' years of experience, stays away from mathematical proofs, and instead focuses on the insights to be gained by careful plotting and modelling of data. In particular the chapters on causal modelling, and the challenges of working with selected samples, provide some desperately needed lessons.' David Spiegelhalter, University of Cambridge
'Gelman and Hill, have done it again, this time with Aki Vehtari. They have written a textbook that should be on every applied quantitative researcher’s bookshelf. Most importantly they explain how to do and interpret regression with real world, complicated examples. Practicing academics in addition to students will benefit from giving this book a close read.' Christopher Winship, Harvard University, Massachusetts
'Comprehensive and charming, this regression manual belongs on every regressor’s shelf.' Joshua Angrist, Massachusetts Institute of Technology
Product details
September 2020Paperback
9781107676510
548 pages
245 × 189 × 30 mm
1.06kg
183 b/w illus. 215 exercises
Available
Table of Contents
- Preface
- Part I. Fundamentals:
- 1. Overview
- 2. Data and measurement
- 3. Some basic methods in mathematics and probability
- 4. Statistical inference
- 5. Simulation
- Part II. 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 III. Generalized Linear Models:
- 13. Logistic regression
- 14. Working with logistic regression
- 15. Other generalized linear models
- Part IV. Before and After Fitting a Regression:
- 16. Design and sample size decisions
- 17. Poststratification and missing-data imputation
- Part V. 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 VI. What Comes Next?:
- 22. Advanced regression and multilevel models
- Appendices: A. Computing in R
- B. 10 quick tips to improve your regression modelling
- References
- Author index
- Subject index.
