Regression Diagnostics: An Introduction (Quantitative Applications in the Social Sciences) [Paperback]

John Fox (Author)

Book Description

ISBN-10: 080393971X | ISBN-13: 978-0803939714 | Publication Date: August 14, 1991 | Edition: 1

With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that compromise a regression analysis and for determining whether certain assumptions appear reasonable. The book covers such topics as the problem of collinearity in multiple regression, dealing with outlying and influential data, non-normality of errors, non-constant error variance and the problems and opportunities presented by discrete data. In addition, sophisticated diagnostics based on maximum-likelihood methods, scores tests, and constructed variables are introduced.

Editorial Reviews

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

• Paperback: 96 pages
• Publisher: Sage Publications, Inc; 1 edition (August 14, 1991)
• Language: English
• ISBN-10: 080393971X
• ISBN-13: 978-0803939714
• Product Dimensions: 8.7 x 5.4 x 0.2 inches
• Shipping Weight: 4 ounces (View shipping rates and policies)
• Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #238,662 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

3 of 3 people found the following review helpful:

5.0 out of 5 stars Great condensed reference for diagnostic techniques, May 23, 2007

By 

Alethephant (Virginia Beach, VA USA) - See all my reviews

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This review is from: Regression Diagnostics: An Introduction (Quantitative Applications in the Social Sciences) (Paperback)

This book is an ideal, comprehensive short reference for regression diagnostics that has most or all of the techniques in one place. John Fox is the current master guru of regression, and his writings are very authoritative. Very useful desk reference for the practicing statistician, but perhaps not totally accessible to the beginning learner.

4.0 out of 5 stars Good introduction to problems with multiple regression, May 7, 2010

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Steven A. Peterson (Hershey, PA (Born in Kewanee, IL)) - See all my reviews
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This review is from: Regression Diagnostics: An Introduction (Quantitative Applications in the Social Sciences) (Paperback)

Multiple regression is a powerful and useful statistical technique. It and its derivative techniques are workhorses in the social sciences. Much of my own quantitative research is based on the use of regression. There are real strengths of this method: the results have pretty straightforward interpretation; you can control for a wide variety of variables; software (such as SPSS) makes it easy as pie to run.

However, there are problems with multiple regression that the user needs to be aware of. For example, if two independent variables are highly intercorrelated, you may get--as one result--strange results. High intercorrelations are indicators of the dread problem of multicollinearity. Hence, when running a regression, one would want to test for this effect. Thankfully, programs like SPSS allow one to test for multicollinearity in a variety of ways. Just so, outliers (extreme scores) can throw off results. The researcher can check to see if there are such outliers. Other problems for regression discussed in this slender volume: non-normality, heteroscedasticity, and nonlinearity. The good thing is that contemporary statistical software allows one to check these out.

So, a good resource for the person wanting to run multiple regression while making sure that the data do not confound their results.