On 4 Dec 2003 04:59:24 -0800, [EMAIL PROTECTED] (Luis Domingues) wrote: > Hi all, > > My question is this: > What's the apropriated method for find out witch variables ( only > dicotomic ) contribute for a decision? > I have a lot (160) of dic. variables.
And, What do you mean by "contribute to a decision"? You get the univariate test as the 2x2 contingency table (assuming that was a dichotomous decision). You get some other *effect* measure by looking at that statistic instead: odds ratio and beta coefficient are the other two that I have used, but there are other ways of 'standardizing' whatever that effect may be. > Discriminant analysis is the apropriated method? > If yes, how shell i use it? Put all variables? You get some test "while controlling for something" by using some simultaneous approach. Discriminant function does that exactly the same as ordinary regression on a 0/1 criterion. For variable like the ones I usually have, putting in 160 is not the smart way to do it; you don't name your variables. For mine, items like symptom-ratings, there would be high intercorrelation -- so the appropriate approach is data reduction FIRST. If you do intend to use 160 variables simultaneously, it would probably be necessary to have a fuzzy final relationship (no perfect R-squared, or near it), and at least several hundred decisions each of Yes and No (or whatever the dichotomy). > > Is this the correspondente analysis of Principal components and factor > analysis for dicotomic variables? I don't see CA as being valuable for a dichotomy, but you might look up keywords: CART and decision-trees. -- 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/ . =================================================================
