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From |
Christian Weiss <[email protected]> |

To |
[email protected] |

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, <[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 >> > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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