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Synopsis
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...
Book Description
Data 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. It introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages.
About the Author
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...
"About this title" may belong to another edition of this title.
- PublisherCambridge University Press
- Publication date2006
- ISBN 10 052168689X
- ISBN 13 9780521686891
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages648
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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