Regression and Other Stories

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Andrew Gelman, Jennifer Hill, Aki Vehtari: Regression and Other Stories (2020, University of Cambridge ESOL Examinations)

560 pages

English language

Published Nov. 2, 2020 by University of Cambridge ESOL Examinations.

ISBN:
978-1-107-67651-0
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4 stars (1 review)

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.

3 editions

Review of 'Regression and Other Stories' on 'Goodreads'

4 stars

An excellent detailed but practical introduction to Regression from a largely Bayesian point of view with great examples and R code. There are many interesting asides, e.g. regression to the mean, and some key topics are explained in 2 or 3 different ways to aid your understanding. Also, by doing things in both a traditional frequentist - maximum likelihood way and then using stan_glm, the benefits of the Bayesian approach are seen.