search for: 0.02184

Dear all, I am perplexed when trying to get the same results using pairwise.t.test and t.test. I'm using examples in the ISwR library, >attach(red.cell.folate) I can get the same result for pairwise.t.test and t.test when I set the variances to be non-equal, but not when they are assumed to be equal. Can anyone explain the differences, or what I'm doing wrong? Here's an example

> y<-c(131.79, 131.79, 135.02, 135.55, 136.46, 136.83, 137.82, 138.00, 138.06, 138.04, 140.04, 142.44, 145.47, 144.34, 146.30, 147.54, 147.80) > x<-c(194.5, 194.3, 197.9, 198.4, 199.4, 199.9, 200.9, 201.1, 201.4, 201.3, 203.6, 204.6, 209.5,208.6, 210.7, 211.9, 212.2) > fitted.results<-lm(y~x) > summary(fitted.results) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max

Folks; I am having a problem with the cv.glm and would appreciate someone shedding some light here. It seems obvious but I cannot get it. I did read the manual, but I could not get more insight. This is a database containing 3363 records and I am trying a cross-validation to understand the process. When using the cv.glm, code below, I get mean of perr1 of 0.2336 and SD of 0.000139. When using a