Best Andrew Gelman books

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman

Best price for this book: $ 38.97

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Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http://www.stat.columbia.edu/~gelman/arm/

Teaching Statistics: A Bag of Tricks

Andrew Gelman

Best price for this book: $ 25.86

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Students in the sciences, economics, social sciences, and medicine take an introductory statistics course. And yet statistics can be notoriously difficult for instructors to teach and for students to learn. To help overcome these challenges, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, activities, examples, and projects that involve active student participation.

Part I of the book presents a large selection of activities for introductory statistics courses and has chapters such as 'First week of class'-- with exercises to break the ice and get students talking; then descriptive statistics, graphics, linear regression, data collection (sampling and experimentation), probability, inference, and statistical communication. Part II gives tips on what works and what doesn't, how to set up effective demonstrations, how to encourage students to participate in class and to work effectively in group projects. Course plans for introductory statistics, statistics for social scientists, and communication and graphics are provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics, sampling, and data science.

Bayesian Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science)

Andrew Gelman

Best price for this book: $ 57.69

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Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors―all leaders in the statistics community―introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

New to the Third Edition

  • Four new chapters on nonparametric modeling
  • Coverage of weakly informative priors and boundary-avoiding priors
  • Updated discussion of cross-validation and predictive information criteria
  • Improved convergence monitoring and effective sample size calculations for iterative simulation
  • Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
  • New and revised software code

The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do

Andrew Gelman

Best price for this book: $ 10.39

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On the night of the 2000 presidential election, Americans watched on television as polling results divided the nation's map into red and blue states. Since then the color divide has become symbolic of a culture war that thrives on stereotypes--pickup-driving red-state Republicans who vote based on God, guns, and gays; and elitist blue-state Democrats woefully out of touch with heartland values. With wit and prodigious number crunching, Andrew Gelman debunks these and other political myths.


This expanded edition includes new data and easy-to-read graphics explaining the 2008 election. Red State, Blue State, Rich State, Poor State is a must-read for anyone seeking to make sense of today's fractured political landscape.

Statistics Done Wrong: The Woefully Complete Guide

Alex Reinhart

Best price for this book: $ 15

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Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.
Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics.
You'll find advice on:
  • Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan
  • How to think about p values, significance, insignificance, confidence intervals, and regression
  • Choosing the right sample size and avoiding false positives
  • Reporting your analysis and publishing your data and source code
  • Procedures to follow, precautions to take, and analytical software that can help
Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know.
The first step toward statistics done right is Statistics Done Wrong.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)

Richard McElreath

Best price for this book: $ 76.21

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Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.

The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.

By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.

Web Resource
The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan

John Kruschke

Best price for this book: $ 67.11

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Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets.

The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment.

This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.

  • Accessible, including the basics of essential concepts of probability and random sampling
  • Examples with R programming language and JAGS software
  • Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis)
  • Coverage of experiment planning
  • R and JAGS computer programming code on website
  • Exercises have explicit purposes and guidelines for accomplishment
  • Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs

Teaching Statistics: A Bag of Tricks

Andrew Gelman

Best price for this book: $ 59.99

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This volume takes a positive spin on the field of statistics. Statistics is seen by students as difficult and boring, however, the authors of this book have eliminated that theory. Teaching Statistics: A Bag Of Tricks, brings together a complete set of examples, demonstrations and projects that not only will increase class participation but will help to eliminate any negative feelings toward the area of statistics.

A Quantitative Tour of the Social Sciences

Best price for this book: $ 39.99

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To foster a deeper understanding of the interconnection of the social sciences, economists should know where historical data come from, sociologists should know how to think like economists, political scientists would benefit from understanding how models are tested in psychology, historians should learn how political processes are studied, psychologists should understand sociological theories, and so forth. This overview by prominent social scientists gives an accessible, non-technical sense of how quantitative research is done in the social sciences. Upon finishing this book, the reader should have a sense of the different models and different ways of thinking in economics, history, sociology, political science and psychology, which in turn they can bring back to their major field.

By Andrew Gelman Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (Expanded Edition) (Expanded)

Best price for this book: $ 26.04

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