Bob:
I think your commentary is rude.
I'm just trying to help.
Like I said, at the beginning of my posting "This is just an idea".
Perhaps you missed my statement. If you didn't and still made this comment,
ah well.
H.
Bob Wheeler wrote in message <[EMAIL PROTECTED]>...
>Perhaps you missed my statement that the
>efficiency was about 20%. If you didn't and still
>made this comment, ah well.
>
>Hugo Hidalgo wrote:
>>
>> This is just an idea:
>>
>> Could it be that your predictors are intercorrelated, and you are just
>> overfitting the model?
>> Stepwise regression could allow this to happen if the predictors pass the
>> t-test.
>> See:
>> Belsey,1991. Conditioning Diagnostics: Collinearity and Weak data in
>> regression.
>>
>> Hugo
>>
>> Bob Wheeler wrote in message <[EMAIL PROTECTED]>...
>> >His name is Chris Jordan, from "a manufacturing
>> >company." Why he chooses to keep it secret is
>> >anybody's guess, but of course it is rude.
>> >
>> >His problem is that he has calculated a response
>> >using a mathematical formula that apparently is
>> >well represented by a quadratic, and hence R^2 is
>> >near unity -- the difference is likely due to
>> >rounding. It is not a statistical problem. The
>> >design, by the way, is not D-optimal, but rather
>> >has an efficiency of about 20%.
>> >
>> >
>> >Rich Ulrich wrote:
>> >>
>> >> (I am just addressing a single point.)
>> >> On 30 Sep 2000 14:06:59 -0700, [EMAIL PROTECTED] (Donald Burrill)
>> >> wrote:
>> >>
>> >> < concerning >
>> >> > On Sat, 30 Sep 2000 [EMAIL PROTECTED] wrote:
>> >> < snip, most >
>> >> > > My adjusted R square is also very close to R Square.
>> >>
>> >> DB>
>> >> > As is natural for R very close to 1.
>> >>
>> >> - but how close is "very close"? I don't think it can be,
>> >> with 30/31 as the R-squared expected by chance.
>> >>
>> >> Using the adjusted R-squared formula in Cohen and Cohen,
>> >> the distance from 1.0 will be 30 times as big as the observed,
>> >> so that .9954 will be shrunk to .86. Assuming that you do start
>> >> with the full number of variables in the equation, as is usually
>> >> recommended. But you still get "a lot" of shrinkage by my
>> >> book, even if you say the error is (say) only 10 times as
>> >> big as the observed error of 0.0046.
>> >>
>> >> --
>> >> Rich Ulrich, [EMAIL PROTECTED]
>> >> http://www.pitt.edu/~wpilib/index.html
>> >
>> >--
>> >Bob Wheeler --- (Reply to: [EMAIL PROTECTED])
>> > ECHIP, Inc.
>
>--
>Bob Wheeler --- (Reply to: [EMAIL PROTECTED])
> ECHIP, Inc.
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