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.