I haven't read the whole book yet, but I've been using it to prepare for a guest lecture on regression diagnostics for graduate students in epidemiology and public health. So far, I really enjoy the overall approach on regression, and I have learned quite a bit in the process.
I really like the pragmatic approach the authors take. They present regression from the point of view of a practitioner who has a research/policy question to answer. They also provide code samples to recreate the graph and analysis they perform. There is also a strong bias towards "fake-data simulation", i.e. to understand your model, fit it to fake data and see how it performs. As a statistician with a computational bias myself, I was already sold on the idea. But hopefully the book can help convert other practicing statisticians and data analysts.
I definitely recommend this book to anyone who is comfortable with programming in R (or is willing to learn) and who wants to learn more about applied regression.
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Regression and Other Stories (Analytical Methods for Social Research) 1st Edition, Kindle Edition
by
Andrew Gelman
(Author),
Jennifer Hill
(Author),
Aki Vehtari
(Author)
&
0
more Format: Kindle Edition
Amazon Price | New from | Used from |
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.
- ISBN-13978-1107023987
- Edition1st
- PublisherCambridge University Press
- Publication dateJuly 23 2020
- LanguageEnglish
- File size11959 KB
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From the Publisher

Product description
Review
'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 --This text refers to the hardcover edition.
'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 --This text refers to the hardcover edition.
Book Description
A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference. --This text refers to the hardcover edition.
About the Author
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. --This text refers to the hardcover edition.
Jennifer Hill is Professor of Applied Statistics at New York University.
Aki Vehtari is Associate Professor in Computational Probabilistic Modeling at Aalto University, Finland. --This text refers to the hardcover edition.
Product details
- ASIN : B08BKXPP5K
- Publisher : Cambridge University Press; 1st edition (July 23 2020)
- Language : English
- File size : 11959 KB
- Simultaneous device usage : Up to 4 simultaneous devices, per publisher limits
- Text-to-Speech : Not enabled
- Enhanced typesetting : Not Enabled
- X-Ray : Not Enabled
- Word Wise : Not Enabled
- Sticky notes : Not Enabled
- Print length : 548 pages
- Best Sellers Rank: #351,241 in Kindle Store (See Top 100 in Kindle Store)
- #95 in Applied Statistics eBooks
- #95 in Probability & Statistics (Kindle Store)
- #255 in Financial Economic Statistics
- Customer Reviews:
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241 global ratings
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Top reviews from other countries

Graham Dean
2.0 out of 5 stars
Made up data sets
Reviewed in the United Kingdom 🇬🇧 on June 29, 2021Verified Purchase
I liked the explanations, I hated the made up data sets. Regression with real world data is far more challenging than the cosmetic examples in this book. I was looking for a text to support my lecturing, sadly this is not it.

Vikram
5.0 out of 5 stars
Wonderful book
Reviewed in India 🇮🇳 on July 24, 2020Verified Purchase
This is a wonderful book, one of the best on regression, recommended both for those looking for an introduction and for practitioners. Even those who have the earlier book by Gelman and Hill will find much to delight in. The authors show how you can use graphics with fitted regression models, and use simulation for understanding. The fun chapter titles on page xiii are a good guide. Appendix A tells you how to work with R. Appendix B offers invaluable tips, for example: “Forget about statistical significance”, “Do causal inference in a targeted way.” A website gives you all the data and code by example used in the book.


Vikram
Reviewed in India 🇮🇳 on July 24, 2020
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Georg Walther
2.0 out of 5 stars
Kindle edition is not a great read on mobile devices
Reviewed in Germany 🇩🇪 on July 23, 2020Verified Purchase
The Kindle edition comes as statically sized pages - as you would expect regular pdf files to look like only now you're stick with this file in your Kindle app.
If you're going for the electronic version of this book then it's probably a good idea to purchase a proper pdf directly from the publisher so you get to choose where and how you read this.
If you're going for the electronic version of this book then it's probably a good idea to purchase a proper pdf directly from the publisher so you get to choose where and how you read this.
2 people found this helpful
Report

teo_01
5.0 out of 5 stars
Ottimo libro per un primo approccio alla regressione bayesiana
Reviewed in Italy 🇮🇹 on August 20, 2020Verified Purchase
Ottimo, spiega nel dettaglio diversi tipi di regressione con approccio bayesiano