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Re: st: Log Normality of Dependentvar


From   Christian Weiss <christian.weiss@nightberry.de>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Log Normality of Dependentvar
Date   Mon, 8 Jun 2009 19:08:54 +0200

Hi Steven,

thanks a lot for your explanation!

Unforunately, it seems that something of oyur last message got cut off?
Where can I find information on the "power transformation"? (google
does not offer to much in that respect)

Chris


On Mon, Jun 8, 2009 at 6:55 PM, <sjsamuels@gmail.com> wrote:
> the best fitting power transform to normality. But it is not relevant
> to -swilk- with the lnnormal option, because the power transform may
> not be a log (power =0) and the command does not subtract off a shift
> parameter.
>
> -Steve
>
> On Mon, Jun 8, 2009 at 12:38 PM, <sjsamuels@gmail.com> wrote:
>> -Chris--
>>
>> -lnskew0-- finds  by iteration a value of k for which y= ln(x - k) has
>> skewness zero.  The manual implies that with the "lnnormal" option,
>> -swilk- , estimates "k" by the method of -lnskew0-.  In fact, the ado
>> file for -swilk- does not call -lnskew0-, but instead computes an
>> approximation.. This probably accounts for the discrepancy that you
>> observed.
>>
>> Analyses of  ln(var) and of the transformation  -bcskew0- are
>> irrelevant to -swilk-, because the 'lnnormal" option considers the
>> hypothesis of a three-parameter lognormal distribution.   I presume
>> that by "skskew0"  you meant  "lnskew0
>>
>> -Steve
>>
>> On Mon, Jun 8, 2009 at 6:18 AM, Maarten buis<maartenbuis@yahoo.co.uk> 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-.
>>>
>>>
>>
>> On Mon, Jun 8, 2009 at 5:38 AM, Christian
>> Weiss<christian.weiss@nightberry.de> wrote:
>>>
>>> testing my dependent var via swilk or sfrancia rejects the Null
>>> Hypothesis of Normality.
>>> However, using the "lnnormal" option of swilk accepts the nully
>>> hypothesis - it seems that the dependent variable is lognormal
>>> distributed.
>>>
>>>
>>> Suprisingly,after transformim my dependent variable by ln(var) or by
>>> skskew0 / bcskew0, swilk still rejects the null hypothesis of
>>> normality.
>>>
>>> How can that be explained?
>>>
>>> ..puzzled...Chris
>>
>
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