
An R Companion to Applied Regression
SAGE Publications Inc (Publisher)
2nd Edition
Published on 26. January 2011
Book
Paperback/Softback
472 pages
978-1-4129-7514-8 (ISBN)
Article exhausted; check for reprint
Description
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 accompanying website for the book can be found at http:/socserv.mcmaster.ca/jfox/Books/Companion/index.html provides:
R scripts for examples by chapter
Data files used in the book
The car package (Companion to Applied Regression), an accompanying software for regression diagnostics and other regression-related tasks
Other resources to help students get the most out of the text
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 accompanying website for the book can be found at http:/socserv.mcmaster.ca/jfox/Books/Companion/index.html provides:
R scripts for examples by chapter
Data files used in the book
The car package (Companion to Applied Regression), an accompanying software for regression diagnostics and other regression-related tasks
Other resources to help students get the most out of the text
Reviews / Votes
"The text is very clearly written. It contains much wisdom and useful hints for those trying to analyze data with R." -- Robert W. HaydenMore details
Edition
2nd Revised edition
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Edition type
Revised edition
Product notice
Paperback (trade)
Dimensions
Height: 254 mm
Width: 179 mm
Thickness: 27 mm
Weight
834 gr
ISBN-13
978-1-4129-7514-8 (9781412975148)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions

John Fox | Sanford Weisberg
An R Companion to Applied Regression
Book
10/2018
3rd Edition
SAGE Publications Inc
€157.00
Shipment within 15-20 days
Previous edition

Book
07/2002
1st Edition
SAGE Publications Inc
€75.70
Article exhausted; check for reprint
Persons
John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.
Sanford Weisberg is Professor Emeritus of statistics at the University of Minnesota. He has also served as the director of the University's Statistical Consulting Service, and has worked with hundreds of social scientists and others on the statistical aspects of their research. He 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 60 articles in a variety of areas, his methodology research has primarily been in regression analysis, including graphical methods, diagnostics, and computing. He is a fellow of the American Statistical Association and former Chair of its Statistical Computing Section. He is the author or coauthor of several books and monographs, including the widely used textbook Applied Linear Regression, which has been in print for almost forty years.
Sanford Weisberg is Professor Emeritus of statistics at the University of Minnesota. He has also served as the director of the University's Statistical Consulting Service, and has worked with hundreds of social scientists and others on the statistical aspects of their research. He 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 60 articles in a variety of areas, his methodology research has primarily been in regression analysis, including graphical methods, diagnostics, and computing. He is a fellow of the American Statistical Association and former Chair of its Statistical Computing Section. He is the author or coauthor of several books and monographs, including the widely used textbook Applied Linear Regression, which has been in print for almost forty years.
Content
Preface
1. Getting Started With R
2. Reading and Manipulating Data
3. Exploring and Transforming Data
4. Fitting Linear Models
5. Fitting Generalized Linear Models
6. Diagnosing Problems in Linear and Generalized Linear Models
7. Drawing Graphs
8. Writing Programs
References
Author Index
Subject Index
Command Index
Data Set Index
Package Index
About the Authors
1. Getting Started With R
2. Reading and Manipulating Data
3. Exploring and Transforming Data
4. Fitting Linear Models
5. Fitting Generalized Linear Models
6. Diagnosing Problems in Linear and Generalized Linear Models
7. Drawing Graphs
8. Writing Programs
References
Author Index
Subject Index
Command Index
Data Set Index
Package Index
About the Authors