27. Unusual Data. Leverage:
Analysis, edited by John Fox and J. Scott Long. (Reprinted with revisions from. Sociological Methods and Research 13:510-542.) Draper, Norman R., and Harry Smith. 1998. Applied Regression Analysis. (3rd ed.) New York: Wiley. Fox, John. 1991. Regression Diagnostics. Newbury Park, CA: Sage. Fox, John and J. Scott
Bibliography. Summary. Explaining the techniques needed for exploring problems that comprise a regression analysis, and for determining whether certain assumptions appear reasonable, this book covers such topics as the problem of collinearity in multiple regression, non-normality of errors, and discrete data. Contents.
"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
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
2. NOTE: The R content presented in this document is mostly based on an early version of Fox, J. and Weisberg, S. (2011) An. R Companion to Applied Regression, Second Edition, Sage; and from class notes from the ICPSR's workshop Introduction to the R Statistical Computing Environment taught by John Fox during the
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,
Robust Regression. Appendix to An R and S-PLUS Companion to Applied Regression. John Fox. January 2002. 1 M-Estimation. Linear least-squares estimates can behave badly when the The most common general method of robust regression is M-estimation, introduced by Huber (1964).1 . on regression diagnostics.

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February 2018