In article <[EMAIL PROTECTED]>,
[EMAIL PROTECTED] wrote:
> What you do mean when you say, "I have two groups of samples"? ....
> How does this differ from having one large group of samples?
>
> Hartley's will *always* take into account the respective means, in
> the sense that the variance are computed around individual means; I
> don't know what your comment means, " ... without taking into account
> their respective means."
My original question was a bit confusing. I'm going to try to clarify
it without beating it to death. My hunch is that the Hartley's F-max
test is too simplistic for what I want to accomplish but here it goes.
There are two populations, let's say "A" and "B" populations where the
population means are not equal a priori. We have a group of k samples
of n observations of each respective population, k is the same for both
populations and n is the same for both populations - this is a
simplification but you get the point. I've got three potential results
1) the Hartley's F-max test for the group of samples of population "A"
is significant at the 95% level and the Hartley F-max test for the
group of samples of population "B" is not significant at the 95% level,
2) the reverse of #1 or 3) neither F-max test is significant. So what
I'm asking is in the 3rd case, if "A"'s F-max statistic is significant
at the 70% level and "B"'s F-max statistic is significant at the 40%
level could I then say that the samples of the "A" population are
"more" heteroscedastic that the samples of the "B" population or would
this be a meaningless statement? And finally could one say that there
is a "significant" difference in heteroscedasticity between the "A"
samples than the "B" samples based soley on the difference between the
F-max statistics? Of course if one or the other is significant at the
95% level then it's a "no brainer" but even in that case is it possible
to compare the F-max statistics assuming the means are not equal a
priori?
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