A few comments/questions:
I am yet another observer of this thread and have read the
suggested papers and have a few comments.
a. I am not a scholar of pure mathematical statistics nor
theoretical mathematics, and am not convinced either way since I have yet
to see some concrete logical frame work that backs up the 'for' and the
'against' sides. (and yes, Bill, I have read your paper(s). For
example, one commentor made the comment that Bill's model "contradicts
the quadratic formula" without showing proof. (remember there are those
of us who would like to see the proof for these types of statements)
b. If you are trying to define causality, do you not have to have
knowledge of what exactly may or may not affect your outcome? For
example, the growth of an orange may be correlated to the growth of and
apple but one is NOT the cause of the other. Now, the causes of growth
(water, food, energy) may be similar in each type of fruit, but how each
fruit grows may in fact be completely independent of each other.
c. Bill, I would have to say that if you responded in a manner
less hostile, you may get the information you seek. Jumping down
someones throat every time they contradict you is what is burying you and
your ideas. And, I need not be reminded that you have been at this for
years and that the whole of Academia is out to get you, because frankly,
I don't give a damn.
d. If you have the variables Y, X1 and X2, correlation tells you
how similar each variable is to each other, not how X1 and X2 predicts or
causes Y. Correlation does not assume an outcome.
More questions:
If Y = X1 + X2 in the Chamber's model, are each of the X1 and X2
polarizing? If so is there a chance that the sum of the X's can equal no
outcome? Would this create extra variance in the predictability of the
model? What shape are the residual distributions in the CC or CR?
later...
sean.
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