Re: st: Output from SKTEST

[Maarten Buis <maartenlbuis@gmail.com>]

On Sat, Mar 31, 2012 at 11:03 PM, David Hoaglin wrote:
> Before you use tests based on sample skewness and kurtosis, it would
> be a good idea to look at a normal probability plot for each of the
> variables.  A histogram may also be useful, but it is not sufficient,
> because it will not show you enough about the tails.  You should look
> for outliers, as well as evidence of multimodality.

I agree. A nice compromise between the histogram and a normal
probability plot is the hanging rootogram. In Stata these can be drawn
with the -hangroot- package, which is available from SSC (-ssc install
hangroot-). Examples can be found on
<http://www.maartenbuis.nl/software/hangroot.htm>. In general, I would
not see these graphs as competitors but as complementary ways of
inspecting the data: each graph is good at highlighting different
aspects.

Moreover, if these variables are part of a regression(-like) model
than you are typically not interested in the uni-variate
normality/Gaussianity. If your variable is the
dependent/explained/left-hand-side/y variable than an assumption of
minor importance  is that the variable is normally distributed
_conditional_ on the independent/explanatory/right-hand-side/x
variable(s). To check that assumption you can after a linear
regression inspect the residuals or use -margdistfit- (also available
from SSC: -ssc install margdistfit-). Examples of the latter package
can be found here:
-http://www.maartenbuis.nl/software/margdistfit.html-. If the variable
is an independent/explanatory/right-hand-side/x-variable than the
distribution is even less important. Its only use is that it can
sometimes give a clue on the functional form of the relationship
between that x and y variable.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany


http://www.maartenbuis.nl
--------------------------

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