Dear all a couple of months ago i've found threads regard test that verify AnOVa assumption on homogeneity of variances. Prof. Ripley advice LDA / QDA procedures, many books (and many proprietary programs) advice Hartley's F_max, Cochran's minimum/maximum variance ratio (only balanced experiments), K^2 Bartlett's test, Levene's test.
Morton B. Brown and Alan B. Forsythe in a 1974 article wrote about "Robust test for the equality of variances" (editet by Journal of the American Statistical Association Vol. 69, pp.: 364-367) "...the common F-ratio and Bartlett�s test are very sensitive to the assumption that the underlying populations are from a Gaussian distribution. When the underlying distributions are nonnormal, these tests can have an actual size several times larger than their nominal level of significance...." Peter Armitage in Statistical Methods in Medical Research ( Blackwell Scientific Publication, 1971, page. 212) "...Bartlett's test maybe is less useful than it seems; motif are two: first F test is very sensitive to the nonnormality; second, in samples with few data, true variances must differ in considerable manner before there is a wise/reasonable probability to obtain results significant. In other word, even if M/C ratio is NOT significant, estimated variances and true variances can differ in substantial manner. If eventually differences in true variances had weight in further analysis, is more clever admit differences, even if tests give a non significant result..." So, I'm asking at gurus which is best behaviour, which test they use or teach. ------------------------------------------------------------------------------------------------------------------------- Landini dr. Massimiliano Tel. mob. (+39) 347 140 11 94 Tel./Fax. (+39) 051 762 196 e-mail: numero (dot) primo (at) tele2 (dot) it ------------------------------------------------------------------------------------------------------------------------- Legge di Hanggi: Pi� stupida � la tua ricerca, pi� verr� letta e approvata. Corollario alla Legge di Hanggi: Pi� importante � la tua ricerca, meno verr� capita. ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
