On Thu, 1 Nov 2001, Chia C Chong wrote:
> I am a beginner in the statistical analysis and hypothesis. I have 2
> variables (A and B) from an experiment that was observed for a certain
> period time. I need to form a statistical model that will model these
> two variables.
Seems to me you're asking in the wrong place. The _model_ cannot be
determined statistically, nor (in general) by statisticians. It arises
from the investigator's knowledge of the substantive area in which the
experiment was carried out, and of the reasons why the experiment was
designed & conducted in the first place. Given a model, or, better, a
series of more or less complex models, a statistician can help you decide
among them, and can help you arrive at numerical values for (at least
some of) the parameters of the models.
> As an initial step, I plot the histograms of A & B separately to
> see how the data were distributed.
How would you (or the investigator) expect them to be distributed?
In particular, why would you think they might follow any of the usual
theoretical distributions? (In other words, what's the theory behind
your expectations -- or your lack of expectations?)
> However, it seems that both A & B can't be easily described by a simple
> statistical distributions like Gaussian, uniform etc via visualisation.
> Hence, I proceeded to plot the Quantile-Quantile plot (Q-Q plot)
What did you think this would tell you?
> and trying to the fit both A and B with some theoretical distributions
> (all distributions avaiable in Matlab!!). Again, none of the
> distributions seem can descibe then completely. Then I was trying to
> perform the Wilcoxon Rank Sum test.
What hypothesis were you testing, and why was the Wilcoxon test relevant
to it?
> From the data, it seems that A & B might be correlated in some sense.
You have not described a scatterplot of A vs. B (or B vs. A, whichever
pleases you). Why not?
> My question is, what can I purely rely on the Wilcoxon Rank Sum Test to
> find the parameters of the distributions that can describe A & B??
Since the Wilcoxon is allegedly a distribution-free test, I'm quite
bemused by the idea that it might help one _find_ parameters...
> How do perform test to see whether A & B are really correlated??
Practically all pairs of variables are correlated, to one degree or
another. What will it signify to you if A and B are (or are not)
"really" correlated (whatever "really" is intended to mean)?
> How if A or/and B are overlay of two or more distributions??
Hmm. By "overlay", do you mean "mixture", perhaps?
> Can this test tell me?? What make thing more tricky is that clustering
> was also observed in both A & B.
At the same times, or in the same places?
> I really hope to get an idea how to start with the statistical analysis
> for this kind problem...#
I'm sorry, but I don't yet perceive precisely what the problem is that
the data were intended (or designed?) to address.
-- DFB.
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Donald F. Burrill [EMAIL PROTECTED]
184 Nashua Road, Bedford, NH 03110 603-471-7128
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