On Mon, 6 Oct 2003 23:40:50 -0700, "albinali" <[EMAIL PROTECTED]> wrote:
> 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? Linear regression is not one of the *legitimate* options when you have several non-ordered categories. Say, Hindu=1, Islam=2, Christian=3, Jewish =4 - and you want to predict the average? No, that won't do. Ordinary logistic regression has only two categories, so it won't work, either, except as a series of 0/1 contrasts. You could use linear regression with 0/1 contrasts, too. Two programs. For separating several groups using several 'predictors' that are continuous variables, discriminant function (DF) gives the clearer side-statistics, showing means for the groups on separate variables, and multivariate centroids in 'reduced space'; and the clearer warnings (or clues) when assumptions are being violated, including over-fitting. Multinomial logistic regression (MLR) might provide a more precise solution, with nicer theoretical properties, if your group Ns are all large enough, and if your groups are rather distinct. MLR is newer (as a part of stat-packages), and has some popularity for that reason. It allows you to describe the relationships as odds ratios -- if that appeals to you. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html "Taxes are the price we pay for civilization." . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
