In article <[EMAIL PROTECTED]>, Rich Ulrich <[EMAIL PROTECTED]> wrote: >On Wed, 8 Oct 2003 10:33:49 +0200, "Lughnasad" <[EMAIL PROTECTED]> >wrote:
>> "albinali" <[EMAIL PROTECTED]> escribi en el mensaje >> news:[EMAIL PROTECTED] >> > Hi, >> > With non-ordinal categorical data, I was told that logistic regression >> is >> > likely to do a better job, why is that, whats the problem with linear >> > regression? >> In the case of what the dependient variable be dichotomous, if you do a >> multiple regression you are violating the assumptions required for >> inference. In particular the regression errors are not normally distribuited >> neither their variance is constant. >Comment on the comment - >Since the multiple regression on a dichotomy is >mathematically identical to the problem of Fisher's >discriminant function, the multiple regression is pretty >robust for the job. You can do t-tests on dichotomous >variables and the tests will be pretty accurate, too, >and those are the same shape of residuals. This is the case if the INDEPENDENT variable is dichotomous. The KEY assumption for any kind of validity of a linear regression is that the "errors" are uncorrelated with the independent variables. If this is not essentially the case, the results of a linear regression are decidedly biased. Lack of homoscedasticity means that one can do better by using weights, and lack of independence of the errors means that one can get improvement in other ways, but lack of normality of the errors just means that the overused tests of significance, etc., are not quite right. >> If you do a multiple regression the final conclusions can be misleading. Same problem here. -- This address is for information only. I do not claim that these views are those of the Statistics Department or of Purdue University. Herman Rubin, Department of Statistics, Purdue University [EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558 . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
