On 8 Oct 2003 12:15:17 -0500, [EMAIL PROTECTED] (Herman
Rubin) wrote:

> In article <[EMAIL PROTECTED]>,
> Rich Ulrich  <[EMAIL PROTECTED]> wrote:
> >On Wed, 8 Oct 2003 10:33:49 +0200, "Lughnasad" <[EMAIL PROTECTED]>
> >wrote:
[ snip a bit ]

ru > >Comment on the comment -
> >Since the multiple regression on a dichotomy is 
> >mathematically identical to the problem of Fisher's 
> >discriminant function, the multiple regression is pretty
> >robust for the job.  You can do t-tests on dichotomous
> >variables and the tests will be pretty accurate, too, 
> >and those are the same shape of residuals.

HR > 
> This is the case if the INDEPENDENT variable is dichotomous.

It should be clear from the context that I am describing
the DEPENDENT ...

> 
> The KEY assumption for any kind of validity of a linear
> regression is that the "errors" are uncorrelated with the
> independent variables.  If this is not essentially the
> case, the results of a linear regression are decidedly
> biased.  

 ... and the  *test*  is not notably disturbed by bias.
There is an evident  *meaning*  to the LR coefficients :  that
is one potential advantage of using Logistic with two groups,
instead of using Regression on 0/1  or Fisher's discriminant
function.  I can't say that I am disturbed by 'bias'  when I 
don't have much notion of how to interpret the prediction
in any case.

>             Lack of homoscedasticity means that one can do
> better by using weights, and lack of independence of the
> errors means that one can get improvement in other ways,
> but lack of normality of the errors just means that the
> overused tests of significance, etc., are not quite right. 

'not quite right'  but still, pretty damned good.  The LR
alternative tends to bomb [ perhaps I should say, 'fail to
achieve asymptotic properties' ]  without warning;  that 
shortcoming makes 'not quite right'   OLS into a competitive 
alternative.   OLS  is robust, easier to read, and has useful
side-statistics.   (LR  is working to catch up.)


> 
> >> If you do a multiple regression the final conclusions can be misleading.
> 
> Same problem here.

-- 
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
"Taxes are the price we pay for civilization." 
.
.
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