<br /><p>Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the <b>Second Edition</b> of <b>Applied Regression Analysis and Generalized Linear Models</b> 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. <br /><br /><b>Key Updates to the Second Edition</b>:</p><ul><li>Provides greatly enhanced coverage of generalized linear models, with an emphasis on models for categorical and count data</li><li>Offers new chapters on missing data in regression models and on methods of model selection</li><li>Includes expanded treatment of robust regression, time-series regression, nonlinear regression, and nonparametric regression</li><li>Incorporates new examples using larger data sets</li><li>Includes an extensive Web site at that presents appendixes, data sets used in the book and for data-analytic exercises, and the data-analytic exercises themselves </li></ul><p><b>Intended Audience: </b><br />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.</p><p> </p>