Gus said:
>Here is how I interpret what you've said to date:
>1. If you take two uniformly distributed random variables x1 and x2 and
>form
> the sum y = x1 + x2, then y has a distribution that is not uniform.
>2. If you have two variables x and y and want to determine whether x
>depends
> on y or y depends on x, first select the x variable uniformly, then
>run
> two regressions, one with each of the two variables as the IV. The y
> variable is not going to be uniform, of course, but according to you
> this proves causality.
>
>What have I got wrong?
Bill responded:
Well you left out a whole lot of stuff. I have another paper (virus free
that may help) I will send to you on request that explains things more
simply, But the essence was expressed in my recent post in which I explained
the polarization effect, Simply running regressions is not the point, Its
what kind of regressions (or correlatins), The simplest expression of the
effect is that the correlations between the two independent variables X1 and
x2 will be opposite in the extremes versus midranges of y (the dependent
variable), The correlations between x1 and x2 will not be opposite across
the ranges of either x variable,
Does this make sense?
Bill
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