From: John Sorkin <jsorkin_at_grecc.umaryland.edu>

Date: Wed 06 Apr 2005 - 12:54:40 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Apr 06 13:02:55 2005

Date: Wed 06 Apr 2005 - 12:54:40 EST

Please forgive a straight stats question, and the informal notation.

let us say we wish to perform a liner regression: y=b0 + b1*x + b2*z

There are two ways this can be done, the usual way, as a single
regression,

fit1<-lm(y~x+z)

or by doing two regressions. In the first regression we could have y as
the dependent variable and x as the independent variable
fit2<-lm(y~x).

The second regrssion would be a regression in which the residuals from
the first regression would be the depdendent variable, and the
independent variable would be z.

fit2<-lm(fit2$residuals~z)

I would think the two methods would give the same p value and the same beta coefficient for z. The don't. Can someone help my understand why the two methods do not give the same results. Additionally, could someone tell me when one method might be better than the other, i.e. what question does the first method anwser, and what question does the second method answer. I have searched a number of textbooks and have not found this question addressed.

Thanks,

John

John Sorkin M.D., Ph.D.

Chief, Biostatistics and Informatics

Baltimore VA Medical Center GRECC and

University of Maryland School of Medicine Claude Pepper OAIC

University of Maryland School of Medicine
Division of Gerontology

Baltimore VA Medical Center

10 North Greene Street

**GRECC (BT/18/GR)
**

Baltimore, MD 21201-1524

410-605-7119

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jsorkin@grecc.umaryland.edu

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R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Apr 06 13:02:55 2005

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