On Thu, Sep 9, 2010 at 10:22 PM, cpblpublic firstname.lastname@example.org wrote:
I am looking for some reaally basic statistical tools. I have some sample data, some sample weights for those measurements, and I want to calculate a mean and a standard error of the mean.
Here are obvious places to look:
numpy scipy.stats statsmodels
It seems to me that numpy's "mean" and "average" functions have their names backwards. That is, often a mean is defined more generally than average, and includes the possibility of weighting, but in this case it is "average" that has a weights argument. Can these functions be merged/renamed/deprecated in the future? It's clear to me that "mean" should allow for weights.
I think of weighted mean and weighted average, pretty much as synonyms, changing names would break backwards compatibility without any real benefit, in my opinion.
None of these modules, above, offer standard error of the mean which incorporates weights. scipy.stats.sem() doesn't, and that's the closest thing. numpy's "var" doesn't allow weights. There aren't any weighted variances in the above modules.
for weighted statistics, I usually refer to ticket 604
but I didn't see weighted sem in it
Again, are there favoured codes for these functions? Can they be incorporated appropriately in the future?
Most immediately, I'd love to get code for weighted sem. I'll write it otherwise, but it might be crude and dumb...
just a thought, I still have to check the details:
Estimating statsmodels.WLS with just a constant should give all the result statistics on the mean, e.g. bse for variance of constant, t() for t-statistic
Thanks! Chris Barrington-Leigh UBC
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