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On Fri, Dec 19, 2008 at 1:05 PM, Sturla Molden <sturla@molden.no> wrote:
On 12/19/2008 6:51 PM, Robert Kern wrote:
How does the current version strike you?
http://docs.scipy.org/numpy/docs/numpy.core.fromnumeric.std/ http://docs.scipy.org/numpy/docs/numpy.core.fromnumeric.var/
It looks accurate. :)
Also it mentions that ddof=0 gives the ML estimate, which is often overlooked.
A warning about what ddof=1 may/will do to the standard error of the variance would also be useful. Estimating the variance unbiased can be equivalent of throwing away a substantial portion of the data; which in turn may translate to a lot of lost investment in work and money.
Why would you be throwing away data if you use a different normalization? I think the only serious point about the degrees of freedom correction is when using the distribution of the estimator, e.g. for testing, and there the ddof is given by the statistical theory. Wether an estimate for the variance or standard deviation in a report is normalized by N or N-1 doesn't really matter, given the randomness of the statistical problem, at least I never checked what normalization the author used. Josef