Re: Linear regression

Thanks Bob for your prompt answer.

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That's the usual assumption about the ERRORS of the conditional
distribution of Y given X. More specifically iid N(0, sigma^2) is
the usual assumption.
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There's nothing to investigate until AFTER you've fitted some tentative
model. THEN, you should examine the residuals (observed errors) to
validate the iid assumptions.
So my model should be Xb=y+e and e=Xb-y should be "close to" iid N(0,
sigma^2).

<skip>
By "couplings" I think you mean either statistical or deterministic
relations some pairs of X's. Short answer, as long as the X's are linearly
independent, there's nothing to worry about.
By couplings I mean some unknown relations between the columns. Unknown
in the sence that no-one have investigated such relations properly, but
based on "intelligent" guess some relations surely exists.

<skip>
You seem to have the common misconceptions and misunderstanding
between "linear indepdence" (linear algebra) and stochastic
(statistical) independence of the error terms.
There are several threads in sci.stat.math specifically about the
difference of these two concepts.
Yes indeed I need to try to understand this topic a better manner.
Actually my current observations are only a subset of a larger set.
I'll return later with more specific questions.


Best regards,
Jens

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