An R Companion to Applied Regression

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This is a broad introduction to the R statistical computing environment in the context of applied regression analysis. It is a thoroughly updated edition of John Fox� s bestselling text An R and S-Plus Companion to Applied Regression (SAGE, 2002). The Second Edition is intended as a companion to any course on modern applied regression analysis. The authors provide a step-by-step guide to using the high-quality free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, enhanced coverage of R graphics and programming, and substantial web-based support materials.

An R Companion to Applied Regression Review

This book would be a great single volume introduction to R for social scientists and others who do a lot of work with regression models. As may be obvious from the title, it is also a great companion to anyone learning about regression models, e.g., in a statistics course. And if you are more experienced but not deeply knowledgeable about regression diagnostics, you will gain a very solid and valuable grounding in those.

The book has three particularly salient features: (1) the first 150 pages are a very nicely encapsulated introduction to R with hands-on examples that highlight many of the things that are covered in more detail later in the book. If you want to learn R and do regression models, this material is the perfect background. (2) the remainder of the book presents those models in more depth, building up piece by piece and with clear social science examples. Of special note is the attention to regression diagnostics -- which are often mentioned in other texts but not presented in such a practical way as here, with clearly worked examples and interpretations. (3) much of the content relies on the authors' R package "car", which provides a great set of tools for regression models, especially plotting, confidence interval estimation, and model diagnostics.

As an experienced R user, I enjoyed the book more than I expected: it taught me some very useful things about diagnostic tools, and demonstrated the "car" [companion to applied regression] package in a convincing way. I've known about car for years, but hadn't used it much; now I expect to use it regularly, especially for plotting.

Although the book calls itself a "companion" to other texts, it is actually self-contained if you already understand the basics of regression models in general. It avoids mathematical exegesis and focuses instead on exactly how to get things done in R ... and even more importantly, on how to understand what R is doing, how to interpret the results and work with the resulting objects, and how to avoid common problems. I'm going to start highly recommending this text to others who are new to R.

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