Hi Marteen,
thank you a lot for your elaboration on this topic! Although this was
very interesting for me, my actual question is still not answered yet.
So let me rephrase: If a variable is lognormally distributed
(according to swilk, lnnormal), why is it not "normally" distributed
after transforming it via ln / skskew0 / bcskew0 (according to swilk)
Best regards
Christian
On Mon, Jun 8, 2009 at 12:18 PM, Maarten buis<[email protected]> wrote:
>
> --- On Mon, 8/6/09, Christian Weiss wrote:
>> testing my dependent var via swilk or sfrancia rejects the
>> Null Hypothesis of Normality.
>
> This is problematic for a number of reasons:
>
> 1) Regression never assumes that the dependent variable is
> normally distributed, except when you have no explanatory
> variables. It only assumes that the residuals are normally
> distributed.
>
> 2) Testing for the normality of the residuals should only
> be done once you are confinced that the other assumptions
> have been met, as violations of the other assumptions are
> likely to lead to residuals that look non-normal
>
> 3) The normality of the residuals is probably the least
> important of the regression assumptions, as regression
> is reasonably robust to violations of it.
>
> 4) Tests are probably not the best way to assess whether
> the errors are normaly distributed. Graphical inspection
> is usually more informative and powerful, see:
> -help diagnostic plots- and -ssc d hangroot- for tools
> to help with that.
>
> For a more general set of tools to perform post-estimation
> checks of regression assumptions see:
> -help regress postestimation-.
>
> Hope this helps,
> Maarten
>
> -----------------------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
>
>
>
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