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From |
Nick Cox <n.j.cox@durham.ac.uk> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: query on testing uniform distributions |

Date |
Tue, 1 Nov 2011 14:08:37 +0000 |

Good point. However, if a uniform model were to be modified, then any more refined model would imply unequal expected frequencies and scaling would then be indicated once more. Also, a motivation for Pearson residuals (although I don't think Pearson ever used them) is, as you know, that they can be thought as a decomposition of a chi-square statistic; such a motivation can be useful for some readerships as explaining what was done, even if the test itself is not carried out. Nick n.j.cox@durham.ac.uk Maarten Buis On Tue, Nov 1, 2011 at 9:53 AM, Nick Cox wrote: > The expected frequencies here are easy to calculate, so I'd move to > Pearson residuals, (observed - expected) / sqrt(expected) and also > plot those against day of year to see what fine structure there is. > The quantile plot is a good starting point, but needs to be followed > up. Just an extra detail on the extra detail: In this case, where the model is a uniform distribution, the division by sqrt(expected) would just rescale all residuals by the same number. I would probably look at the raw residuals, as that way the picture would be exactly the same, but I would find the scale of the residuals to be easier to interpret. * * 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/

**References**:**st: query on testing uniform distributions***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: query on testing uniform distributions***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: query on testing uniform distributions***From:*Maarten Buis <maartenlbuis@gmail.com>

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