Politics & Social Sciences | Political Philosophy

Data Analysis Using Regression and Multilevel/Hierarchical Models

by Andrew Gelman

2020-07-25 01:53:17

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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 ... Read more
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/ Less

Book Details

File size9.9x6.9x1.4inches
Print pages625
PublisherCambridge University Press
Publication date December 1, 2006
ISBN9780521686891
autor-image
Andrew Gelman
Author
Andrew Gelman is professor of statistics and political science at Columbia University. His books include Teaching Statistics: A Bag of Tricks. He received the Presidents' Award in 2003, awarded each y...

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