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From |
"Dan Weitzenfeld" <dan.weitzenfeld@emsense.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Dependent var is a proportion, with large spike in .95+ |

Date |
Wed, 3 Sep 2008 13:22:14 -0700 |

Hi Statalist, I am trying to determine which testing factors drive a proportion dependent variable, PercentNoise. In searching the archives, I came across -betafit-, and a link to the FAQ: "How do you fit a model when the dependent variable is a proportion?" In that response, Allen McDowell and Nic Cox write, "In practice, it is often helpful to look at the frequency distribution: a marked spike at zero or one may well raise doubt about a single model fitted to all data." That describes my situation exactly: I have a marked spike in my histogram at the top bin, roughly .95 - 1. I am wondering how to account for this. Does -betafit- take such a possibility into account? Can someone briefly describe how I could use multiple models to fit all the data, as implied in the FAQ response? My fallback is setting a pass/fail bar and converting my proportions to a binary, then using probit/logit. But the obvious drawback is that I am throwing away information by collapsing the continuous (albeit bounded) proportion variable to a binary. Thanks in advance for any suggestions, Dan * * 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/

**Follow-Ups**:**st: RE: Dependent var is a proportion, with large spike in .95+***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**Re: st: Dependent var is a proportion, with large spike in .95+***From:*David Airey <david.airey@Vanderbilt.Edu>

**Re: st: Dependent var is a proportion, with large spike in .95+***From:*David Airey <david.airey@Vanderbilt.Edu>

**st: RE: Dependent var is a proportion, with large spike in .95+***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

**Re: st: Dependent var is a proportion, with large spike in .95+***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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