Re: st: non-normal residuals

[Xixi Lin <winnielxx@gmail.com>]

Thanks Nick,

You are very helpful. ^_^

Best,
Xixi Lin

On Tue, Feb 12, 2013 at 10:44 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> 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 <winnielxx@gmail.com> 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)
>> <jvverkuilen@gmail.com> 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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