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. As an initial step, I plot the histograms of A & B separately to
> see how the data were distributed. 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) 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. From the data, it seems
> that A & B might be correlated in some sense.
If the data are (positively) correlated, do not use the
Wilcoxon-Mann-Whitney rank sum test; use the sign test on the
differences, which will usually be much more powerful in the presence of
significant correlation.
If the two populations differ (roughly) only by translation, the
differences may well be (roughly) symmetrically distributed. Then you
may get more power yet by using the signed ranks test on the differences
(confusingly, this is also named for Wilcoxon).
IN MINITAB: (data in C1, C2)
let C3 = C1-C2
wtest c3
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