From: Ted Harding <Ted.Harding_at_nessie.mcc.ac.uk>

Date: Mon 05 Dec 2005 - 20:08:41 GMT

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> I don't follow this. I agree with the first line (though I prefer to

*> write it differently), but I don't see how it leads to the second. For
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*> example, consider a distribution which is equally likely to be +/- 1,
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*> and a sample from it consisting of a single 1 and a single -1. The
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*> first formula gives 1 (which is the variance), the second gives 2.
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*>
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*> The second formula is unbiased because in a random sample I am just as
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*> likely to get a 0 from the second formula, but I'm curious about what
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*> you mean by "this comes to".
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*>
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*> Duncan
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E-Mail: (Ted Harding) <Ted.Harding@nessie.mcc.ac.uk> Fax-to-email: +44 (0)870 094 0861

https://stat.ethz.ch/mailman/listinfo/r-devel Received on Tue Dec 06 07:20:37 2005

Date: Mon 05 Dec 2005 - 20:08:41 GMT

On 05-Dec-05 Duncan Murdoch wrote:

>> The variance of X is (or damn well should be) defined as >> >> Var(X) = E(X^2) - (E(X))^2 >> >> and this comes to (Sum(X^2) - (Sum(X)/N)^2))/(N-1).

> I don't follow this. I agree with the first line (though I prefer to

Sorry, you're of course right -- I was being a bit hasty and maganed to tangle this with a standard definition of the "variance" of a finite population which uses the 1/(N-1) divisor!

E-Mail: (Ted Harding) <Ted.Harding@nessie.mcc.ac.uk> Fax-to-email: +44 (0)870 094 0861

Date: 05-Dec-05 Time: 20:08:38 ------------------------------ XFMail ------------------------------ ______________________________________________R-devel@r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-devel Received on Tue Dec 06 07:20:37 2005

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