Mike,
As a demonsrtation to myself, I once fit OLS regression
models to data with (1) a non-uniformly distributed
binary outcome and (2) a continuous outcome with a
U-shaped distribution. I then used the same models to
estimate the parameter standard errors using a naive
bootstrap. The distribution of the bootstrap parameter
estimates in both cases was normal (judging from the
Q-Q plots and standard tests of normality). Normality
of parameter estimates isn't everything--so I am not
suggesting that you use OLS regression indiscriminantly.
But some people apparently believe there is no way for
parameter estimates to be normally distributed when the
data are not. That simply is not the case.
BTW, do you really have a book stating that the data
need be normally distributed in order to satisfy the
assumptions of OLS regression? I wouldn't be happy
with that book.
In article <8ffek1$1q2$[EMAIL PROTECTED]>, [EMAIL PROTECTED] says...
>I would like to obtain a prediction equation using linear regression for
>some data that I have collected. I have read in some stats books that
>linear regression has 4 assumptions, 2 of them being that 1) data is
>normally distributed and 2) constant variance. In SAS, I have run
>univariate analysis testing for normality on both my dependent and
>independent variable (n=147). Both variables have distributions that are
>skewed.
--
----------------------------------------------
Steve Gregorich
University of California, San Francisco
Center for AIDS Prevention Studies
Medical Effectiveness Research Center
Center for Aging in Diverse Communities
74 New Montgomery Street
San Francisco CA 94105
[EMAIL PROTECTED]
http://sites.netscape.net/gregorich/index.html
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