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Data Analysis Using Regression and Multilevel/Hierarchical Models PDF

651 Pages·2007·8.782 MB·English
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by Andrew Gelman, Jennifer Hill| 2007| 651 pages| 8.782| English

About Data Analysis Using Regression and Multilevel/Hierarchical Models

I got this book while working on an article that involved a hierarchical model with a binary dependent variable - after poking through Radenbush/Bryk and a variety of other texts that left me frustrated. Not only did this book teach me how to properly specify and estimate the model in R, I also learned a lot about interpretation and graphical means of presenting results. I don't think I've read another book that so effectively combines theoretical and practical information, while also being a relatively smooth read - the examples are clear and interesting! In addition to the extensive treatment of hierarchical models, Gelman and Hill also cover non-hierarchical OLS and ML models, plus a variety of other key stats topics. My only quibble is that the accompanying R code on Gelman's website isn't complete - but the fact that they have sample code available at all puts this far beyond most stats books. I wish I had had this book in grad school and look forward to referring to it for years to come.

Detailed Information

Author:Andrew Gelman, Jennifer Hill
Publication Year:2007
ISBN:9780521867061
Pages:651
Language:English
File Size:8.782
Format:PDF
Price:FREE
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