Regression Diagnostics: An Introduction / Edition 1

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Overview

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.

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Product Details

  • ISBN-13: 9780803939714
  • Publisher: SAGE Publications
  • Publication date: 8/14/1991
  • Series: Quantitative Applications in the Social Sciences Series , #79
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 96
  • Sales rank: 1,053,553
  • Product dimensions: 5.56 (w) x 8.48 (h) x 0.18 (d)

Meet the Author

John Fox is the Senator William Mc Master Professor of Social Statistics in the Sociology Department of Mc Master 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..

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Table of Contents

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
Recommendations

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