Applied Regression Analysis, Linear Models, and Related Methods

"I have never read a book on regression that reflects as broad and profound a grasp of the concepts of statistics as this book does. In every topic John Fox deals with--and he does not avoid the slippery ones--he shows a clarity and depth of understanding that goes beyond anything else I have seen in textbooks and that matches the works of the leading researchers within each field."

--Georges Monette, Department of Mathematics and Statistics, York University

"The selection of examples throughout the book is one of its strengths, as they are generally quite engaging in ''real-world'' interest, and demonstrate the practical use (and limitations) of the statistical methods far better than contrived data. I appreciate the fact that John Fox describes what each example ''means'' in terms of the substantive problem behind the data--students would find this quite useful."

--Michael Friendly, Psychology Department, York University

Aimed at researchers and students who want to use linear models for data analysis, John Fox's book provides an accessible, in-depth treatment of regression analysis, linear models, and closely related methods. Fox incorporates nearly 200 graphs and numerous examples and exercises that employ real data from the social sciences. He begins the book with a concise consideration of the role of statistical data analysis in social research. He next covers graphical methods for examining and transforming data, linear least-squares regression, dummy-variables regression, and analysis of variance. Fox also explores diagnostic methods for discovering whether a linear model fit to data adequately represents the data; extensions to linear least squares, including logit and probit models, time-series regression, nonlinear regression, robust regression, and nonparametric regression; and empirical methods for assessing sampling variation, including the bootstrap and cross-validation. More difficult material is segregated in separate sections and chapters and several appendixes are also included presenting background information. Scholars, professionals, researchers, and students in research methods, evaluation, education, sociology, and psychology will appreciate the enhanced and thorough treatment that regression analysis, linear models, and other related methods have received by author John Fox.

I appreciated the balance between technical information and readability. The book offers the reader the ability to gain in depth knowledge about regression analysis and linear models, yet presents the information in an understandable way.

Other texts have proven too simplistic to answer important questions or too complex to understand. This book is a nice balance of both features.


For the Statistically Savvy Only   April 30, 2002
10 out of 10 found this review helpful

I am now using this textbook for a graduate statistics course. I personally do not find it to be the most accessible book for those who are not already highly schooled in statistics, linear algebra, and calculus. There is an attempt at the back of the book to introduce you to the only linear algebra and calculus you "need" to understand the book. But I think the book continues to go far beyond what is accessible to someone being introduced to this information for the first time.

I think the book is probably excellent if you are already familiar with regression, calculus, and linear algebra. However, for those who are not, I would recommend Paul Allison's "Multiple Regression: A Primer" to get OLS and Pampel's "Logistic Regression: A Primer" to get Logistic. These books introduce you to the same concepts but without all the extra stuff that most people won't use anyway.