
Regression Diagnostics
An Introduction
John Fox(Author)
SAGE Publications Inc (Publisher)
1st Edition
Published on 8. October 1991
Book
Paperback/Softback
96 pages
978-0-8039-3971-4 (ISBN)
Article exhausted; check for reprint
Description
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.
More details
Series
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 216 mm
Width: 140 mm
Weight
113 gr
ISBN-13
978-0-8039-3971-4 (9780803939714)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Book
05/2020
2nd Edition
SAGE Publications Inc
€65.79
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Person
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.
Content
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
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