All of the statistical packages that I am currently using and have used in the past (Matlab, Minitab, R, S-plus) calculate standard deviation using the sqrt(1/(n-1)) normalization, which gives a result that is unbiased when sampling from a normally-distributed population. NumPy uses the sqrt(1/n) normalization. I'm currently using the following code to calculate standard deviations, but would much prefer if this could be fixed in NumPy itself:
def mystd(x=numpy.array([]), axis=None): """This function calculates the standard deviation of the input using the definition of standard deviation that gives an unbiased result for samples from a normally-distributed population.""" xd= x - x.mean(axis=axis) return sqrt( (xd*xd).sum(axis=axis) / (numpy.size(x,axis=axis)-1.0) ) -- View this message in context: http://old.nabble.com/non-standard-standard-deviation-tp26566843p26566843.html Sent from the Numpy-discussion mailing list archive at Nabble.com. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
