Applied Regression Analysis, Linear Models, and Related Methods [Hardcover]

John Fox (Author)

Book Description

ISBN-10: 080394540X | ISBN-13: 978-0803945401 | Publication Date: February 5, 1997

An accessible, detailed, and up-to-date treatment of regression analysis, linear models, and closely related methods is provided in this book. Incorporating nearly 200 graphs and numerous examples and exercises that employ real data from the social sciences, the book begins with a consideration of the role of statistical data analysis in social research. It then moves on to cover the following topics: graphical methods for examining and transforming data; linear least-squares regression; dummy-variables regression; analysis of variance; 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

Editorial Reviews

Review

"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 that matches the works of the leading researchers within each field."  (Georges Monette )

About the Author

John Fox is the Senator William McMaster Professor of Social Statistics in the Sociology Department of McMaster University in Hamilton, Ontario, Canada. Professor Fox earned a Ph.D. in sociology from the University of Michigan in 1972. He has delivered numerous lectures and workshops on statistical topics, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the annual meetings of the American Sociological Association, and the Oxford Spring School in Quantitative Methods for Social Research. He has written many articles on statistics, sociology, and social psychology, and is the author of several books on statistics, including most recently Applied Regression Analysis and Generalized Linear Models, Second Edition (Sage, 2008) and A Mathematical Primer for Social Statistics (Sage, 2009), and (with Sanford Weisberg) An R Companion to Applied Regression, Second Edition (Sage, 2011). Professor Fox is an active contributor to the R Project for Statistical Computing and is a member of the R Foundation. His work on this book was partly supported by a grant from the Social Sciences and Humanities Research Council of Canada.

Product Details

• Hardcover: 624 pages
• Publisher: Sage Publications, Inc (February 5, 1997)
• Language: English
• ISBN-10: 080394540X
• ISBN-13: 978-0803945401
• Product Dimensions: 10.1 x 7.2 x 1.5 inches
• Shipping Weight: 2.7 pounds
• Average Customer Review: 3.6 out of 5 stars  See all reviews (7 customer reviews)
• Amazon Best Sellers Rank: #797,035 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

Most Helpful Customer Reviews

11 of 11 people found the following review helpful:

3.0 out of 5 stars For the Statistically Savvy Only, April 29, 2002

By A Customer

This review is from: Applied Regression Analysis, Linear Models, and Related Methods (Hardcover)

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.

5 of 5 people found the following review helpful:

5.0 out of 5 stars Get it now!!! Best on the subject., January 15, 2004

By 

Julio C. Cerono (BALCARCE, BUENOS AIRES Argentina) - See all my reviews
(REAL NAME)   

This review is from: Applied Regression Analysis, Linear Models, and Related Methods (Hardcover)

Dr. Fox makes an excellent contribution to the student community across geographies. The text is an excellent balance between theory and practical applications of the linear regression methodology. The author is extremely clear in explaining not only simple and multiple linear regression, but also topics such as bootstraping, logistic and other regression techniques for non normal response variables. The book do not fall down near your toes: the topics are covered in a depth that is amenable for a PhD student.
It is very interesting also to look at the many side comments and suggested readings that the author introduces many times in the book. I congratulate Dr. Fox for this clear, understandable and easy to follow text.

4 of 4 people found the following review helpful:

4.0 out of 5 stars Good but Flawed, May 18, 2005

By 

Davar314 (San Francisco, CA) - See all my reviews

Amazon Verified Purchase

This review is from: Applied Regression Analysis, Linear Models, and Related Methods (Hardcover)

Dr. Fox has written in a thoughtful original manner. Example, pretty much all regression books starts out with a graph of simple linear regression model statisfying all the strong assumptions that went into it. Dr. Fox starts out by showing graph of data that violates every single assumption. This is the sort of innovative and creative approach that shows what is best about this book. Dr. Fox has a deep conceptual understanding of this material.

The book doesn't get 5 stars becuase a significant flaw. Dr. Fox (or perhaps the publishers) wanted every kind of student to be able to read this book. Both students with advanced and also students with no statistical/mathematical expertise and sophistication. The result is a fragmented text. For instance, the geometrical interpretation of least squares fit is not integrated into the initial discussion (it comes 130 pages later!). If it was integrated then many of the derivations and discussions would be far simpler and intuitive. This sepration allows a student with no linear algebra background to read this text but it also wastes the time of the advanced students who have to wait for the more simpler and intuitive approach.