Data Analysis Using Regression and Multilevel / Hierarchical Models
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Data Analysis Using Regression and Multilevel / Hierarchical Models by Andrew Gelman
Book DescriptionData Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, 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.
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Book DetailsISBN: 9780521686891
(253mm x 177mm x 35mm)
Imprint: Cambridge University Press
Publisher: Cambridge University Press
Publish Date: 18-Dec-2006
Country of Publication: United Kingdom
Books By Author Andrew Gelman
Red State, Blue State, Rich State, Poor State, Paperback (December 2009)
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. This title debunks these and other political myths. It includes easy-to-read graphics explaining the 2008 election. It is suitable for those seeking to make sense of fractured political landscape.
Quantitative Tour of the Social Sciences, Paperback (April 2009)
In this book, prominent social scientists describe quantitative models in economics, history, sociology, political science, and psychology.
Data Analysis Using Regression and Multilevel / Hierarchical Models, Hardback (December 2006)» View all books by Andrew Gelman
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
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Author Biography - Andrew Gelman
Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published over 150 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002). Jennifer Hill is Assistant Professor of Public Affairs in the Department of International and Public Affairs at Columbia University. She has co-authored articles that have appeared in the Journal of the American Statistical Association, American Political Science Review, American Journal of Public Health, Developmental Psychology, the Economic Journal and the Journal of Policy Analysis and Management, among others.
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