It may help to bear in mind that the PREDICTED value is a function ONLY of the INDEPENDENT variables. (Many people seem to lose sight of this fact, or not to notice it in the first place: perhaps a confusion arising from labelling the predicted value "Y-hat", which might lead the naive to suppose that it was somehow a function of the dependent variable; of course, it is not, whatever is left of the dependent variable being contained in the residuals.)
It would be possible to argue further that the only information of interest (for these diagnostic purpopses -- heteroscedasticity and all) about the predictors is contained in the fitted function of them that is the predicted value. One is not so much interested in observing, say, heteroscedasticity of the residuals w.r.t. X_1, as in whether any such heteroscedasticity survives (or persists?) into the predicted-value variable. On Wed, 19 Nov 2003, Jennifer Bo wrote: > I have been reading a lot of articles on empirical studies using > multiple regreession. Many scientists use a scatterplot of the > standardized residuals and standardized PREDICTED values to test for > homoskedasticity. If there was no pattern they concluded that the > variance of the residuals was costant across all level of the > INDEPENDENT variables (homoskedasticity). > > This confuses me. Wouldn't I have to use scatterplots of EACH of the > INDEPENDENT variables and the residuals to make sure there is no > heteroskedasticity? ----------------------------------------------------------------------- Donald F. Burrill [EMAIL PROTECTED] 56 Sebbins Pond Drive, Bedford, NH 03110 (603) 626-0816 . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
