On Mon, 2003-09-29 at 09:45, Rich Ulrich wrote: > On Thu, 25 Sep 2003 14:56:52 GMT, "seferiad" <[EMAIL PROTECTED]> > wrote: > > > I want to compare the variance of two "different" data sets for hypothesis > > testing. If I assume the distributions of both data sets were normal, then > > I simply apply the F-test. But I do not want to assume that the > > distributions were derived from normal populations. > > If you want to compare the *variances*, the simplest F-test > uses the ratio of the variances. That is sensitive, as you > suggest (if that is what you are suggesting) to non-normality. > The usual alternative is to compute the absolute deviations > from the median (or from the mean, for convenience) > and do a simple ANOVA on those numbers. Levene test.
I've found the Ansari-Bradley test to be useful for a non-parametric comparison of scale (non-paramtric equivalent of std. dev, sort of). References (stolen shamelessly from the R help page for ansari.test() - http://www.r-project.org). Myles Hollander & Douglas A. Wolfe (1973), Nonparametric statistical inference. New York: John Wiley & Sons. Pages 83-92. David F. Bauer (1972), Constructing confidence sets using rank statistics. Journal of the American Statistical Association 67, 687-690. Cheers Jason -- Indigo Industrial Controls Ltd. http://www.indigoindustrial.co.nz +64-(0)21-343-545 . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
