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From |
Phil Schumm <pschumm@uchicago.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Choosing a family using glm |

Date |
Tue, 24 Aug 2010 17:38:22 -0500 |

On Aug 24, 2010, at 4:14 PM, Phil Schumm wrote:

To my understend in a clasical linear regression the asumption ofnormality is in the distribution of the error term, but in glm theasumption defined by the family selection is on the distribution ofthe dependent variable. Isnt that a huge cost for using glm insteadof a clasical linear regression model?You are laboring under a misunderstanding. To say that thedistribution of Y conditional on X is Normal with mean XB andvariance sigma^2 is the same as saying that the distribution of theerrors (i.e., Y - XB) is Normal with mean 0 and variance sigma^2.And to emphasize the GLM approach, what is most important (if you'refitting a linear regression) is that the mean is XB and the varianceis constant (i.e., that your assumptions about the first and secondmoments are correct).

-- Phil * * 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**:**Re: st: Choosing a family using glm***From:*Laurie Molina <molinalaurie@gmail.com>

**References**:**st: Choosing a family using glm***From:*Laurie Molina <molinalaurie@gmail.com>

**Re: st: Choosing a family using glm***From:*Phil Schumm <pschumm@uchicago.edu>

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