--- Joanne Marshall wrote:
> I have run a regression and find out the standard error of one particular
> variable X is 0.13.
>
> i understand it is an estimate of the standard deviation in the unobservable
> affecting y, equivalently, it estimates the standard deviation in y, after
> the effect of X has been taken out.
>
> However, I am not sure how i can illustrate the 0.13. would it be consider
> as small S.E?
I think you mixing up what is sometimes called "the standard error of the
estimate" (in my humble opinion a complete misnomer) with the standard error.
In stata the standard error of the estimate is called Root MSE. Whether or
not a residual variance is large depends on how much variance there was in y
before the regression. One way to see whether this is big is to compare the
variance that is explained with the total variance of y. This is what the R
square does, it is the proportion of explained variance.
The standard error is something completely different. You estimate a regression
on a random sample. So you could have drawn a different sample and than would
have gotten an different estimate for the effect of x on y. The uncertainty in
the estimate of the effect of x on y is captured in the standard error. Imagine
you would draw many samples and in each sample you would estimate the effect of
y on x, than you would get many different estimates of that effect. If your
model is correct you would on average find the correct parameter. The standard
deviation of that distribution of is the standard error. To see whether that is
large or small you typically look at the column p> |t|.
Hope this helps,
Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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