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 a?? Offers new chapters on missing data in regression models and on methods of model selection a?? Includes expanded treatment of robust regression, time-series regression, nonlinear regression, and nonparametric regression a?? Incorporates new examples using larger data sets a?? 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. High Praise for the First Edition a??Even though the book is written with social scientists as the target audience, the depth of material and how it is conveyed give it far broader appeal. Indeed, I recommend it as a useful learning text and resource for researchers and students in any field that applies regression or linear models (that is, most everyone), including courses for undergraduate statistics majors.... The author is to be commended for giving us this book, which I trust will find a wide and enduring readership.a?? a??JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION a??[T]his wonderfully compre