Applied Regression Analysis and Generalized Linear Models

by John D. Fox

Linear models, their variants, and extensions are among the most useful and widely used statistical tools for social research. The Second Edition of Applied Regression Analysis and Generalized Linear Models provides an accessible, in-depth, modern treatment of regression analysis, linear models, and closely related methods. Author John Fox makes the text as user-friendly as With the exception of three chapters, several sections, and a few shorter passages, the prerequisite for reading the book is a course in basic applied statistics that covers the elements of statistical data analysis and inference. Even relatively advanced topics (such as methods for handling missing data and bootstrapping) are presented in a manner consistent with this prerequisite. Key Features of the Second Edition Covers regression models--such as generalized linear models, limited-dependent-variable-models, mixed models and Cox regression--and methods that are increasingly being used in social science research Contains a more robust Web site with extensive appendices of background material (matrices, linear algebra, vector geometry; calculus; probability and estimation); data sets used in the book and for data analytic exercises; and the data-analytic exercises themselves. Incorporates real data from the social sciences that is similar to data readers are likely to encounter. This book should be of interest to students and researchers in the social sciences, as well as other disciplines that employ linear models for data analysis, and in courses on applied regression and linear models where the subject matter ofapplications is not of special concern.

Genres: Nonfiction, Science, Mathematics, Sociology, Psychology

Hardcover

First published April 16, 2008

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

[May Ling · Rating 2 out of 5]

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. 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 vectors.



However, the organization is poor. Linear algebraic, matrices and vectors should be introduced in the more accurate place of chapter 2 or 3. Further, as a teaching tool, this book offers a lack of practice problems to help the student through the learning process. Further, each topic is addressed so quickly and with a single, kind of contrived example, that it would be difficult for most newbies to the field to really obtain the type of practice and deep understanding that is required to go onto the next topic with confidence.



Higher rated texts should split up the topics into multiple books or provide a greater number of examples and problems for students to build their skill set.

[J.R. · Rating 2 out of 5]

It's a book about applied regression and GLMs, who on earth would rate it higher than two stars?

[Michael · Rating 3 out of 5]

My worst nightmare

[Ngalula Fleurant · Rating 2 out of 5]

I don't like the author's choice of language. The concept is difficult to grasp when the author doesn't use lament terms

[Qingyan WANG · Rating 3 out of 5]

Does anyone know if there is any solution manual book to the following exercises?