- "The book's careful yet mathematically accessible style is generously illustrated with examples and graphical displays, making it ideal for either classroom use or self study. It appears destined to adorn the shelves of a great many applied statisticians and social scientists for years to come." -- Brad Carlin, Department of Biostatistics, University of Minnesota

- "Simply put, Data Analysis Using Regression and Multilevel/Hierarchical Models is the best place to learn how to do serious empirical research. Gelman and Hill have written a much needed book that is sophisticated about research design without being technical. Data Analysis Using Regression and Multilevel/Hierarchical Models is destined to be a classic!" -- Alex Tabarrok, Department of Economics, George Mason University

- "Gelman and Hill have written what may be the first truly modern book on modeling. Containing practical as well as methodological insights into both Bayesian and traditional approaches, Applied Regression and Multilevel/Hierarchical Models provides useful guidance into the process of building and evaluating models. For the social scientist and other applied statisticians interested in linear and logistic regression, causal inference and hierarchical models, it should prove invaluable either as a classroom text or as an addition to the research bookshelf." -- Richard De Veaux, Department of Mathematics and Statistics, Williams College

- "The theme of Gelman and Hill's engaging and nontechnical introduction to statistical modeling is 'Be flexible.' Using a broad array of examples written in R and WinBugs, the authors illustrate the many ways in which readers can build more flexibility into their predictive and causal models. This hands-on textbook is sure to become a popular choice in applied regression courses. -- Donald Green, Department of Political Science, Yale University

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Data and software for the examples in the book

Table of contents and index

Some tips on teaching a course based on this book

Errata

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