David Winsemius comcast.net> writes:
>
> It's really not that difficult to get the variance covariance matrix.
> What is not so clear is why you think differential weighting of a set
> that has a perfect fit should give meaningfully different results than
> a fit that has no weights.
Aga
Peter Dalgaard biostat.ku.dk> writes:
>
> The point is that R (as well as almost all other mainstream statistical
> software) assumes that a "weight" means that the variance of the
> corresponding observation is the general variance divided by the weight
> factor. The general variance is still
David Winsemius comcast.net> writes:
>
> Which means those x, y, and "error" figures did not come from an
> experiment, but rather from theory???
>
The fact is I am trying to compare the results of:
(1) lm under R and
(2) the Java applet at http://omnis.if.ufrj.br/~carlos/applets/reta/reta
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