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From |
Lloyd Dumont <lloyddumont@yahoo.com> |

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

Subject |
st: OLS on a Small Sample |

Date |
Wed, 5 Jun 2013 14:36:44 -0700 (PDT) |

Hello, Statalist. A colleague of mine asked me to give him some feedback on a paper. In it, he runs OLS on a sample of 27 of the 31 plants in a single company, predicting performance as a function two independent variables (and a constant, of course). (Four plants are excluded for idiosyncratic reasons, e.g., one makes an oddball product, one refused to share data, etc.) Assuming all of the other regression assumptions hold, should the relatively small sample size lead me to call these results into question? I mean, how reliable are the standard errors and resulting t-tests when n = 27? And, assuming others are as uncomfortable as I am, is there an obvious nonparametric alternative to OLS for this situation? If nothing else, he could use bootstrap standard errors instead of the standard variance estimator, right? Thank you for your thoughts. Lloyd Dumont * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: OLS on a Small Sample***From:*Nick Cox <njcoxstata@gmail.com>

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