Applied Regression Analysis and Generalized Linear Models

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.

Linear models, their variants, and extensions are among the most useful and widely used statistical tools for social research. The Second Edition of Applied Regression Analysis and Generalized Linear Models provides an accessible, in-depth, modern treatment of regression analysis, linear models, and closely related methods.

Author John Fox makes the text as user-friendly as possible: With the exception of three chapters, several sections, and a few shorter passages, the prerequisite for reading the book is a course in basic applied statistics that covers the elements of statistical data analysis and inference. Even relatively advanced topics (such as methods for handling missing data and bootstrapping) are presented in a manner consistent with this prerequisite.

Key Features of the Second Edition

• Covers regression models--such as generalized linear models, limited-dependent-variable-models, mixed models and Cox regression--and methods that are increasingly being used in social science research
• Contains a more robust Web site with extensive appendices of background material (matrices, linear algebra, vector geometry; calculus; probability and estimation); data sets used in the book and for data analytic exercises; and the data-analytic exercises themselves.
• Incorporates real data from the social sciences that is similar to data readers are likely to encounter.

This book should be of interest to students and researchers in the social sciences, as well as other disciplines that employ linear models for data analysis, and in courses on applied regression and linear models where the subject matter ofapplications is not of special concern.

John Fox is Professor of Sociology at McMaster University in Hamilton, Ontario, Canada. He was previously Professor of Sociology and of Mathematics and Statistics at York University in Toronto, where he also directed the Statistical Consulting Service at the Institute for Social Research. 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 and the annual meetings of the American Sociological Association. His recent and current work includes research on statistical methods (for example, work on three-dimensional statistical graphs) and on Canadian society (for example, a study of political polls in the 1995 Quebec sovereignty referendum). He is author of many articles, in such journals as Sociological Methodology, The Journal of Computational and Graphical Statistics, The Journal of the American Statistical Association, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written several other books, including Applied Regression Analysis, Linear Models, and Related Methods (Sage, 1997), Nonparametric Simple Regression (Sage, 2000), and Multiple and Generalized Nonparametric Regression (Sage, 2000).