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

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

Subject |
RE: st: correlation constant and independent variables |

Date |
Sun, 19 Aug 2007 15:45:17 +0100 |

Roger's main point seems to me to be on target. On a detail: The problem with a constant is that it has zero variance, and that makes its correlation with anything else indeterminate, or undefined, depending on how you want to think of it. Zero covariance is itself not a problem. In other words, the difficulty is in the denominator, not the numerator. Nick n.j.cox@durham.ac.uk Roger Harbord > -coldiag2- doesn't check for correlation, it checks for > collinearity. A > constant doesn't vary so its covariance with any variable is zero and > the correlation isn't defined. > > If you found high collinearity between an independent > variable and the > constant that would imply that the variable's mean is large > compared to > its variance. I'm not sure that would necessarily be a > problem, but you > could consider centering it or otherwise changing its origin to be > closer to its mean if you want to reduce the collinearity. > > frank palme wrote: > > hello list, > > > > i have a simple question which is bothering me. i could not find any > > answer to it in the literature. > > > > i used the coldiag2 command to check for correlation. i found high > > correlation between an independent variable and the > constant. but the > > theoretical part of my thesis clearly suggests to include a > constant. > > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: correlation constant and independent variables***From:*Roger Harbord <rogerharbord@bigfoot.com>

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