Applied Regression Analysis and Generalized Linear Models

John Fox

SAGE, Apr 16, 2008 - Social Science - 665 pages

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Second Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material throughout the book.

Key Updates to the Second Edition:

• Provides greatly enhanced coverage of generalized linear models, with an emphasis on models for categorical and count data
• Offers new chapters on missing data in regression models and on methods of model selection
• Includes expanded treatment of robust regression, time-series regression, nonlinear regression, and nonparametric regression
• Incorporates new examples using larger data sets
• Includes an extensive Web site at http://www.sagepub.com/fox that presents appendixes, data sets used in the book and for data-analytic exercises, and the data-analytic exercises themselves

Intended Audience:
This core text will be a valuable resource for graduate students and researchers in the social sciences (particularly sociology, political science, and psychology) and other disciplines that employ linear and related models for data analysis.

What people are saying

Review: Applied Regression Analysis and Generalized Linear Models

User Review  - May - Goodreads

Now that I have had a few more classes in the subject area, I feel a bit more confident that this book should have an average rating, rather than higher. the explanations of the book are not bad, if ...

Review: Applied Regression Analysis and Generalized Linear Models

User Review  - Michael - Goodreads

My worst nightmare

Contents

• What Is Regression Analysis?: 13
• Examining Data: 26
• LINEAR MODELS AND LEAST SQUARES: 75
• Statistical Inference for Regression: 100
• DummyVariable Regression: 120
• Analysis of Variance: 143
• Statistical Theory for Linear Models: 187
• Unusual and Influential Data: 241
• Diagnosing NonNormality Nonconstant Error Variance and Nonlinearity: 267
• Collinearity and Its Purported Remedies: 308
• Logit and Probit Models for Categorical Response Variables: 335
• Generalized Linear Models: 379
• EXTENDING LINEAR AND GENERALIZED LINEAR MODELS: 425
• Nonlinear Regression: 451
• Robust Regression: 530
• Missing Data in Regression Models: 548
• Model Selection Averaging and Validation: 607
• Notation: 636
• Author Index: 648
• Data Set Index: 664

About the author

John Fox is the Senator William McMaster Professor of Social Statistics in the Sociology Department of McMaster University in Hamilton, Ontario, Canada. Professor Fox earned a Ph.D. in sociology from the University of Michigan in 1972. He has delivered numerous lectures and workshops on statistical topics, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the annual meetings of the American Sociological Association, and the Oxford Spring School in Quantitative Methods for Social Research. He has written many articles on statistics, sociology, and social psychology, and is the author of several books on statistics, including most recently Applied Regression Analysis and Generalized Linear Models, Second Edition (Sage, 2008) and A Mathematical Primer for Social Statistics (Sage, 2009), and (with Sanford Weisberg) An R Companion to Applied Regression, Second Edition (Sage, 2011). Professor Fox is an active contributor to the R Project for Statistical Computing and is a member of the R Foundation. His work on this book was partly supported by a grant from the Social Sciences and Humanities Research Council of Canada..

Bibliographic information

• Title: Applied Regression Analysis and Generalized Linear Models
• Author: John Fox
• Edition: 2, illustrated
• Publisher: SAGE, 2008
• ISBN: 0761930426, 9780761930426
• Length: 665 pages
• Subjects: Mathematics / Probability & Statistics / Regression Analysis; Social Science / Methodology; Social Science / Research; Social Science / Statistics