ligges at statistik.uni-dortmund.de
Fri May 24 15:25:44 CEST 2002
Ambrosini Alessandro wrote:
> I had a big collection of Web pages. Now I have this collection divided into
> clusters. Every page can be relevant or not. I made a table:
> relevant non relevant
> cluster1 1 20
> cluster2 0 15
> cluster3 3 35
> . . .
> . . .
> . . .
> I cluster1 I have 21 Web pages, 1 relevant and 20 no.
> I want to find if relevant pages tend to stay in some clusters, and so I
> want to find if there is a dipendence relevant-cluster. The problem is that
> I have not much relevant pages for cluster. They are 1,2,3 max 5 for cluster
> and so I can't use Chi- square of Pearson.
> Tell me one thing: suppose to have
> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
> [1,] 0 0 1 0 0 0 0 0 0 1 0
> [2,] 21 20 33 17 12 18 12 10 11 10 28
> In this case every column is a cluster, the first row has the relevant pages
> if I do
> Fisher's Exact Test for Count Data
> data: a
> p-value = 0.5611
> alternative hypothesis: two.sided
> how can I interpret this output? How can I read the p-value?
[This seems to be off topic on this list.]
Well, interpreting a p-value is a *very* basic task in statistics. You
really should read a statistical textbook on inference theory, if you
want to work with statistical tests ...
> Have I to compare it with something?
Your \alpha ?
> In the case of perfect dependence, is p-value=1 ?
You are testing on independency!
The null hypothese is rejected, if p < \alpha.
> Please help me, I can't use any book of statistic in this moment and so I
> cant solve the problem by myself. My work can not go on if I don't solve the
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