--
Look up references to the Box-Cox transformation. That is what you
did when you ran -bcskew0- . "bc"= "Box-Cox". But I have to correct
my correction (You can see where this is heading!) The original
Box-Cox transformation, implemented in -boxcox-, is the one that tries
to transform to normality. -bcskew-, as its name implies, finds the
transformation of the same form which produces zero skewness.
-Steve
On Mon, Jun 8, 2009 at 1:08 PM, Christian
Weiss<[email protected]> wrote:
> 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, <[email protected]> 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, <[email protected]> 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<[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-.
>>>>
>>>>
>>>
>>> On Mon, Jun 8, 2009 at 5:38 AM, Christian
>>> Weiss<[email protected]> 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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>>
>
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