Regression and Other Stories
Part of Analytical Methods for Social Research
- Authors:
- Andrew Gelman, Columbia University, New York
- Jennifer Hill, New York University
- Aki Vehtari, Aalto University, Finland
- Date Published: July 2020
- availability: Available
- format: Paperback
- isbn: 9781107676510
Paperback
-
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.
Read more- 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
See more reviews'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
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Date Published: July 2020
- format: Paperback
- isbn: 9781107676510
- length: 548 pages
- dimensions: 245 x 189 x 30 mm
- weight: 1.06kg
- contains: 183 b/w illus. 215 exercises
- availability: 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.
Related Books
also by this author
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org
Register Sign in» Proceed
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.
Continue ×Are you sure you want to delete your account?
This cannot be undone.
Thank you for your feedback which will help us improve our service.
If you requested a response, we will make sure to get back to you shortly.
×