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Regression Diagnostics An Introduction

ISBN-10: 080393971X
ISBN-13: 9780803939714
Edition: 1991
Authors: John Fox
List price: $22.00 Buy it from $16.65
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Description: "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  More...

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Book details

List price: $22.00
Copyright year: 1991
Publisher: SAGE Publications, Incorporated
Publication date: 8/14/1991
Binding: Paperback
Pages: 96
Size: 5.50" wide x 8.50" long x 0.75" tall
Weight: 0.462
Language: English

"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.

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..

Introduction
Linear Least-Squares Regression
Collinearity
Outlying and Influential Data
Non-Normally Distributed Errors
Non-Constant Error Variance
Nonlinearity
Discrete Data
Maximum-Likelihood Methods, Score Tests, and Constructed Variables
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