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52 real-world stories, with hands-on activities, problems, and computer demonstrations in R for learning or teaching regression.

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

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

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.

Based on years of teaching experience, this book provides a wealth of demonstrations, examples, projects and course plans for teachers of statistics courses at all levels. It also includes hints on how to organize and motivate student groups.

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.

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Using examples largely from the authors' own experiences, the book focuses on modern computational tools and obtains inferences using computer simulations. Its unique features include thorough discussions of the methods for checking Bayesian models and the role of the design of data collection in influencing Bayesian statistical analysis.Bayesian Data Analysis offers the practicing statistician singular guidance on all aspects of the subject.

Don Rubin (1943-) is a statistician who has made major contributions in statistical modeling, computation, and the foundations of causal inference. Rubin grew up in the Chicago area, studied physics at Princeton University, and received his PhD at Harvard University in 1970. He has worked at the Educational Testing Service and has taught statistics at the University of Wisconsin, University of Chicago, and Harvard University. He is best known, perhaps, for the expectation-maximization (EM) algorithm (a mathematical framework for iterative optimization, which has been useful for mixture models, hierarchical regression, and many other problems for which closed-form solutions are unavailable); multiple imputation as a method for accounting for uncertainty in statistical analysis with missing data; a Bayesian formulation of instrumental variables analysis in economics; propensity ...

This overview 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.

The Department of Defense Sexual Assault Prevention and Response Office asked the RAND Corporation to conduct an assessment of the rates of sexual assault, sexual harassment, and gender discrimination in the U.S. military. This volume presents survey estimates of how risk of sexual assault and sexual harassment varies across military installations and major commands using data from the 2014 RAND Military Workplace Study.