I'm trying to figure out how Kolmogorov-Smirnov one-sample testing for normality is done in Minitab (or Systat, since the answers apparently match).
If this is my data vector:
abc <- c(0.0313, 0.0273, 0.0379, 0.0427, 0.0286, 0.0327, 0.0298, 0.0381, 0.0559, 0.0573, 0.0558, 0.113, 0.0464, 0.0442, 0.0579, 0.0495)
The boneheaded way of doing this in R would be:
ks.test(abc, pnorm, mean(abc), sd(abc))
Yes, I know that the ks.test help page says to not use the data to estimate the mean/sd of the comparison distribution. Hence, boneheaded. Sidenote - if I understand correctly, SAS is using this as a regular procedure? http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_univariate_sect037.htm
Anyway, the p-value R gives for this improper test is 0.3027, while apparently both Minitab and Systat provide a p-value of 0.029.
The project manager won't hear anything about using other means of testing for normality (or, heavens forbid, use plots of data distribution). At this point I'm just trying to figure out what it is that the other softwares are doing, so that I can explain to myself the differences...
Am I missing something?? If people suggest using simulations instead of the direct test, like here (http://r.789695.n4.nabble.com/Kolmogorov-Smirnov-Test-td3037232.html), would it be possible to include detailed code?