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Re: st: non-normal residuals
From
Nick Cox <[email protected]>
To
[email protected]
Subject
Re: st: non-normal residuals
Date
Tue, 12 Feb 2013 15:44:41 +0000
You can work this out! Generate some normal random deviates. Look at
the -qnorm-. Introduce some skewness. Look at the -qnorm-.
. clear
. set obs 100
obs was 0, now 100
. gen normal = rnormal()
. qnorm normal
. gen lognormal = exp(normal)
Use a sample size that is realistic for your project.
The bottom line is that skewness introduces curvature to the plot.
Nick
On Tue, Feb 12, 2013 at 3:36 PM, Xixi Lin <[email protected]> wrote:
> Thanks for your help. I have a question about the skewness. How to
> tell the skewness from the qnorm plot? Or is there any test that I can
> test the skewness of the residuals?
On Tue, Feb 12, 2013 at 9:41 AM, JVerkuilen (Gmail)
> <[email protected]> wrote:
>> Lots depends on details you've not indicated but I largely agree with
>> Maarten. Non-Gaussianity is in itself not a huge problem, but skew or
>> other clear patterns in the residuals may be a sign that the linear
>> regression is not correct. You may have an omitted variable, for
>> instance. A transformation may help if it makes sense, but I wouldn't
>> do it just to fix up residuals.
>>
>> If you have a reasonable regression model and still have mild
>> non-Gaussianity (which is best diagnosed using -qnorm-) then
>> bootstrapping or robust standard errors is a good fix.
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