Average Reviews:
(More customer reviews)Now that I have had a few more classes in the subject area, I feel a bit more confident that this book should have an average rating, rather than higher. The explanations of the book are not bad, if you already have a thorough understanding of the topic and are using this is a reference. It does provide a quick overview of most of the major topics in the field and includes a full chapter on the treatment of statistical analysis using matrices and graphical vector visuals.
However, the organization is poor. Linear algebra, matrices and vectors should be introduced in the more accurate place of chapter 3,4 versus far later. Further, as a teaching tool, this book lacks practice problems to help the student through the learning process relative to other pieces that I've used. Further, each topic is addressed in the brief, which is good if you know the topic, but bad if it's the first time you're really looking at the work. The examples used are a bit discipline specific, that while not obscure, would make it somewhat difficult for newbies to the field to really obtain the type of practice and deep understanding that is required to go on to the next topic with confidence.
Higher rated texts should split up the topics into multiple books or provide a greater number of examples and problem sets for students to build their skill set. I have seen some that include an accompanying CD of data and practice examples, that can be of great assistance to students struggling to learn this discipline.
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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.
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