Data Analysis Using Regression and Multilevel/Hierarchical Models
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Data Analysis Using Regression and Multilevel/Hierarchical Models

4.37 of 5 stars 4.37  ·  rating details  ·  62 ratings  ·  4 reviews
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 b...more
Paperback, 625 pages
Published December 1st 2006 by Cambridge University Press
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Harlan
Feb 28, 2012 Harlan rated it 4 of 5 stars
Shelves: work
A good comprehensive survey of the topics. But, different sections assume different levels of background knowledge, from nearly nothing to grad-level statistics theory. I like their views on the relative importance of modeling vs. hypothesis testing, and in particular the emphasis on graphs/visualization. Also like the use of R/lmer and BUGS, and am sympathetic to their somewhat critical view of the terminology of mixed-effects models, despite the close connection to their preferred Bayesian vie...more
Asa
This is my favorite statistics book, written by a former professor at the Columbia stats department. Its my number 1 reference book on my desk at work.

Lots of coding examples with R in applied contexts. Great with interpreting model results, model building, and running diagnostics. Limited matrix alegebra is a plus for those of us who arent very interested in the proofs. For quantitative social science stuff if I had to pick one book this would be it.
Jeff Carnegie
So far the overview of regression has been great.
Justin
Excellent reference.
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