Regression Diagnostics: An Introduction

"Its principal themes, sometimes treated independently, include problem-flagging statistics, variable transformations, analytical graphics, and the spirit of Tukey′s exploratory data analysis. Regression Diagnostics. . . combines these themes nicely. . . . The volume is . . . an accurate and detailed portrayal, resulting in a valuable contribution. . . . All in all, this volume is highly recommended not only for systems theorists but also for those sociologists and others desiring an accurate portrayal of feedback concepts. The book is careful and comprehensive . . . and generally brings the reader up to date on the feedback literature."

--Contemporary Sociology

"This excellent, concise, and practical handling of diagnostic methods suffers in no way from its use of social-statistics illustrations. The 80 pages are as good as anything I have seen in promoting, explaining, and illustrating the diagnostic tools for regression."

--Technometrics

Linear least-squares regression analysis makes very strong assumptions about the structure of data--and, when these assumptions fail to characterize accurately the data at hand, the results of a regression analysis can be seriously misleading. With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that comprise a regression analysis, and for determining whether certain assumptions appear reasonable. Beginning in Chapter 2 with a review of least-squares linear regression, 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, score tests, and constructed variables are introduced. The book concludes with suggestions on how regression diagnostic techniques can be effectively applied in research, and offers advice on implementing these suggestions through the use of standard statistical computer packages.

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John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.