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 dataOffers new chapters on missing data in regression models and on methods of model selectionIncludes expanded treatment of robust regression, time-series regression, nonlinear regression, and nonparametric regressionIncorporates new examples using larger data setsIncludes 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.