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An R Companion to Applied Regression, by John Fox, Harvey Sanford Weisberg
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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.
- Sales Rank: #97237 in Books
- Brand: Brand: SAGE Publications, Inc
- Published on: 2010-11-29
- Original language: English
- Number of items: 1
- Dimensions: 9.96" h x .96" w x 7.09" l, 1.80 pounds
- Binding: Paperback
- 472 pages
- Used Book in Good Condition
Review
"The text is very clearly written. It contains much wisdom and useful hints for those trying to analyze data with R." (Robert W. Hayden)
About the Author
John Fox is professor of sociology at McMaster University in Hamilton, Ontario, Canada. Fox earned a PhD in sociology from the University of Michigan in 1972, and prior to arriving at McMaster, he taught at the University of Alberta and at York University in Toronto, where he was cross-appointed in the sociology and mathematics and statistics departments and directed the university's statistical consulting service. He has delivered numerous lectures and workshops on statistical topics in North and South America, Europe, and Asia, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the Oxford University Spring School in Quantitative Methods for Social Research, and the annual meetings of the American Sociological Association. Much of his recent work has been on formulating methods for visualizing complex statistical models and on developing software in the R statistical computing environment. He is the author and co-author of many articles, in such journals as Sociological Methodology, Sociological Methods and Research, The Journal of the American Statistical Association, The Journal of Statistical Software, The Journal of Computational and Graphical Statistics, Statistical Science, Social Psychology Quarterly, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written a number of other books, including Regression Diagnostics (SAGE, 1991), Nonparametric Simple Regression (SAGE, 2000), Multiple and General-ized Nonparametric Regression (SAGE, 2000), A Mathematical Primer for Social Statistics (SAGE, 2008), and, with Sanford Weisberg, An R Companion to Applied Regression, Second Edition (SAGE, 2010). Fox also edits the SAGE Quantitative Applications in the Social Sciences (QASS) monograph series.
Sanford Weisberg is Professor of Statistics at the University of Minnesota, Twin Cities.� He is also director of the University’s Statistical Consulting Service for Liberal Arts, and has worked with literally hundreds of social scientists and others on the statistical aspects of their research. �Professor Weisberg earned a BA in Statistics from the University of California, Berkeley, and a Ph.D. also in statistics from Harvard University, under the direction of Frederick Mosteller.� The author of more than sixty articles, his research has primarily been in the areas of regression analysis, including graphical methods, regression diagnostics, and statistical computing.� He is a Fellow of the American Statistical Association and former Chair of its Statistical Computing Section.� He is the author or co-author of several books, including Applied Linear Regression (third edition 2005, Wiley), Residuals and Influence in Regression (with R. D. Cook, 1982, Chapman & Hall), Applied Regression Including Computing and Graphics (with R. D. Cook, 1999 Wiley).� He has several publications in areas that use statistics including archeology, plant sciences, wildlife management, fisheries, and public affairs.
Most helpful customer reviews
27 of 28 people found the following review helpful.
Excellent, not only for regression models but for R in general
By Sitting in Seattle
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.
6 of 6 people found the following review helpful.
Gentle intro to applied linear regression and "car" package
By Sergey Bushmanov
This book is an excellent how-to guide to applied linear regression modelling. Even though it's called an "R companion" to Applied regression, it provides just enough of the two worlds - theory and basics of R programming - to carry out linear regression analysis on a day-to-day basis.
When choosing a book that would refresh my knowledge about linear regression, I was thinking about three:
- Linear models with R, by J. Faraway
- Using R for Introductory Statistics, by J. Verzani and
- An R companion to Applied Regression, Fox et al
I have fixed on the Mr. Fox's book mainly because he is the author of the "car" package and I wanted to learn more about it, mainly in the context of diagnosing linear regression models.
The book has completely met my expectations with solid applied coverage of the following areas:
- Visual data inspection
- Linear regression models
- Generalized linear regression models
- Diagnostics of LM and GLM
I highly recommend this book for two peculiar features. First, it feels like the author builds his knowledge of linear regression together with the reader, thus greatly facilitating learning process (this fact indeed is not surprising as Mr. Fox has been a university professor for years!). Second, the book heavily uses "car" package, of which Mr. Fox is the developer. "Car" package, on the one hand, helps increase the quality of regression models one produces and, on the other, decreases time needed for building a model.
9 of 10 people found the following review helpful.
Great Value
By Amazon User
This is a fantastic book and one of those books that I feel was worth every penny. I bought it to accompany Fox's regression text, so I was disappointed that it didn't correspond perfectly, chapter for chapter. However, it's such a good book that I soon forgave the one drawback. I'll try to update this later on (I took the class last year, so a little hazy on exact points)...I just saw that there were no reviews and wanted to rectify that.
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