Fisher
Ambrosini Alessandro wrote:
Hello.
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
a
[,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.test(a)
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 ?
Almost 0!!! 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 problem.
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