?Hence there is no point in testing the coefficients of e and u in the regression of c on (e,u,e*u,u^2).?
Thanks! Not only will it cut work load in half, but also allow me to put the C,N and S,N results for u*e and u^2 | fold,set on one page in what printers used to call a ?4-up? in the old-days. (See, for example, the 4-up for a1_1 I?ve sent offline.)
In turn, such 4-ups will not only mean less PDF?s for you to look at, but may also reveal possible relations between u*e and u^2 that would otherwise not even be apparent. (I have many questions about such relationships between u*e and u^2, but will hold off until all the 4- ups are done.)
?When the IOT test is not clear, there are many ways to do a proper test of the hypothesis that the p-values come from a Uniform[0,1] distribution ...?
I?m going to wait till all 18 4-ups are completed for fold x method, and if some really interesting but IOT-undecidable cases arise within the 18, I will do the S-W?s using the PERL implementation described here:
(Although seriously, I am interested in learning exactly how the ?a? constants in the S-W numerator are ?generated from [...] means, variances, and covariances [...].
Here?s a dumber-than-usual question about S-W, if you have a moment.
I used the Stata version of S-W back in 2005 to test the original dicodon over-representation data for normality BEFORE t-testing them. (I didn?t t-test anything that wasn?t normal.)
And what I thought S-W was doing was seeing how well the data conformed to the familiar Gaussian or bell curve.
But now we?re talking about S-W measuring departure from a uniform [0,1] distribution (i.e. the ?random backdrop? in the plots you?ve taught me how to construct.
Is testing for fit to a Gaussian curve and testing for departure from a uniform [0.1] distribution the same thing?
If you have time, could you clarify here? I realize it?s elementary, but when you explain something, I tend to understand it more or less immediately (as opposed to explanations by the "usual suspects".)