Applied Regression Analysis and Generalized Linear Models

by John D. Fox

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.

688 pages, Kindle Edition

First published February 5, 1997

About the author

John Fox is the Senator William McMaster Professor of Social Statistics at the Department of Sociology at McMaster University in Canada.

Reviews

[Terran M · Rating 4 out of 5]

You need at least one thorough book on linear regression, and Fox is arguably the gold standard, as he's the author of the R "car" package, derived from his Companion to Applied Regression book, which contains the canonical Type II and III Anova implementation, the variable inflation factor function, and other useful tools. I found the writing in this book to be pretty clear overall.

There are newer editions of this book, but frankly linear regression has not changed much in decades, and this edition is available for 1/10 the cost of the latest one.

[Farhan Samir · Rating 1 out of 5]

It's very thorough in its coverage of linear regression and some related methods but it jumps into rather complicated and unintuitive derivations quickly. Gelman's *Data Analysis Using Regression* gives a more practical introduction.

[Jeffrey · Rating 4 out of 5]

QMSS