Normal distribution test

Hi all,

I am completely new to R and my knowledge of statistics is quite small so I
hope you can help my.
I have three dimensional point data which represents (and this is what I do
not know for sure) a normal distribution. Now I want to test if this is true

I suppose you want to say you have a sample of three-dim data, say
represented be vectors x1,x2,x3,
and your question is if this data (x1|_1,x2_1, x3_1),...,(x1_n,x2_n, x3_n)
are generated by a three-dim multinormal distribution. That is very
simple, a very good
test is to simply say "reject".  I have never seen three-dim data
which are truly
multinormal.  So a better question is to ask if amultinormal
distribution can be an acceptable
approximation, but then we need to know what is your purpose of
analysis! If you are interested in
extremes or extrere quantiles, then a normal approx is never safe.

If you want a statistical test, then a multivariate extension of
shapiro-wilk is in
install.packages("mvnormtest", dep=TRUE)
library(mvnormtest)
?mshapiro.test

kjetil

or not and as I can remember from statistics lessons I can use Chi-Square
test for distribution test. BUT: I have realy no idea how to do this with R
and additionally if my assumptions are correct and if this is possible with
R at all.

Thank you very much in advance for any answer.
Markus

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