You should probably read fortune(117) and fortune(234) (and possibly some of the original discussions that lead to the fortunes). Reading the help page for the SnowsPenultimateNormalityTest function (TeachingDemos package) may also help. If you are happy with the plots, but still feel the need for a "test" of some sort, then you should investigate using the vis.test function in the TeachingDemos package.
Hope this helps, -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.s...@imail.org 801.408.8111 > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r- > project.org] On Behalf Of Bosken > Sent: Tuesday, February 23, 2010 4:13 AM > To: r-help@r-project.org > Subject: Re: [R] Normal distribution (Lillie.test()) > > > Hi, > > Thanks for your reaction; > > How do you come to the decision that my data not is normal distributed? > > With the 69-95-99.7 test and Q-Q plot seems it ok! But these test are > not > exact, they only give you an image. > > Gr. Bosken > -- > View this message in context: http://n4.nabble.com/Normal-distribution- > Lillie-test-tp1565083p1565762.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
