[SciPy-user] information on statistical functions

josef.pktd@gmai... josef.pktd@gmai...
Fri Dec 19 12:20:20 CST 2008

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.


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