ISBN-10:
141297514X
ISBN-13:
9781412975148
Pub. Date:
11/29/2010
Publisher:
SAGE Publications
An R Companion to Applied Regression / Edition 2

An R Companion to Applied Regression / Edition 2

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Overview

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.

Product Details

ISBN-13: 9781412975148
Publisher: SAGE Publications
Publication date: 11/29/2010
Edition description: Second Edition
Pages: 449
Product dimensions: 6.90(w) x 9.90(h) x 1.10(d)

About the Author

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.


Table of Contents

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

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