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Regression Diagnostics: An Introduction (Quantitative Applications in the Social Sciences), by John Fox
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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.
- Sales Rank: #815927 in Books
- Brand: Brand: SAGE Publications, Inc
- Published on: 1991-08-14
- Original language: English
- Number of items: 1
- Dimensions: 8.50" h x 5.50" w x .75" l, .26 pounds
- Binding: Paperback
- 96 pages
- Used Book in Good Condition
About the Author
John Fox is professor of sociology at McMaster University in Hamilton, Ontario, Canada. Fox earned a PhD in sociology from the University of Michigan in 1972, and prior to arriving at McMaster, he taught at the University of Alberta and at York University in Toronto, where he was cross-appointed in the sociology and mathematics and statistics departments and directed the university's statistical consulting service. He has delivered numerous lectures and workshops on statistical topics in North and South America, Europe, and Asia, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the Oxford University Spring School in Quantitative Methods for Social Research, and the annual meetings of the American Sociological Association. Much of his recent work has been on formulating methods for visualizing complex statistical models and on developing software in the R statistical computing environment. He is the author and co-author of many articles, in such journals as Sociological Methodology, Sociological Methods and Research, The Journal of the American Statistical Association, The Journal of Statistical Software, The Journal of Computational and Graphical Statistics, Statistical Science, Social Psychology Quarterly, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written a number of other books, including Regression Diagnostics (SAGE, 1991), Nonparametric Simple Regression (SAGE, 2000), Multiple and General-ized Nonparametric Regression (SAGE, 2000), A Mathematical Primer for Social Statistics (SAGE, 2008), and, with Sanford Weisberg, An R Companion to Applied Regression, Second Edition (SAGE, 2010). Fox also edits the SAGE Quantitative Applications in the Social Sciences (QASS) monograph series.
Most helpful customer reviews
3 of 4 people found the following review helpful.
Great condensed reference for diagnostic techniques
By Alethephant
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.
2 of 3 people found the following review helpful.
Very obtuse and incomplete
By Abacus
This book, given its focus on a narrow subject, is actually pretty long. 90 pages to cover regression diagnostic is a long slog. Yet, the author sates that "because of space considerations... there is no treatment of [autocorrelation]." That is a huge gap as the independence of the residuals is one of the main assumptions of the linear regression model. The author admits that much when he describes the corresponding Gauss-Markov theorem on pg. 40.
Throughout the book there were so many math notations that were undefined that I could not clearly understand what the author was conveying. For instance, on pg. 16 he talks about the Mellows stat associated with the terms Cp and p. Neither are clearly defined. I guess p is number of independent variables (a guess at best). But, I have no idea what Cp stands for. Later on page 24, he defines the h or hat value in a less than transparent way. On page 28, m is undefined. On pages 68, 87, and 89, NID is undefined. On page 89, V is undefined. Given that those terms play important roles in many equations throughout the book, I could not grasp their meaning.
The narrative itself is often perplexing. For instance, on page 25, the author states: "... even if the errors have equal variances..., the residuals do not." Given that residuals and errors represent the same thing, this quote is not readily comprehensible. There are other somewhat confusing sentences within the book.
Occasionally, I came across tests I knew and calculated long hand in Excel. Yet, as he described them I could hardly recognize them. This includes the Breusch and Pagan test and White test on pages 73, and 74.
Two basic guides to overall econometrics do a good job of covering the entire subject including diagnostic tests in a far more transparent way. Those are: Econometrics For Dummies by Roberto Pedace and a A Guide to Econometrics - 4th Edition by Peter Kennedy.
0 of 1 people found the following review helpful.
Good introduction to problems with multiple regression
By Steven Peterson
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.
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