SciPy

scipy.stats.mstats.sem

scipy.stats.mstats.sem(a, axis=0, ddof=1)[source]

Calculates the standard error of the mean (or standard error of measurement) of the values in the input array.

Parameters :

a : array_like

An array containing the values for which the standard error is returned.

axis : int or None, optional.

If axis is None, ravel a first. If axis is an integer, this will be the axis over which to operate. Defaults to 0.

ddof : int, optional

Delta degrees-of-freedom. How many degrees of freedom to adjust for bias in limited samples relative to the population estimate of variance. Defaults to 1.

Returns :

s : ndarray or float

The standard error of the mean in the sample(s), along the input axis.

Notes

The default value for ddof is different to the default (0) used by other ddof containing routines, such as np.std nd stats.nanstd.

Examples

Find standard error along the first axis:

>>> from scipy import stats
>>> a = np.arange(20).reshape(5,4)
>>> stats.sem(a)
array([ 2.8284,  2.8284,  2.8284,  2.8284])

Find standard error across the whole array, using n degrees of freedom:

>>> stats.sem(a, axis=None, ddof=0)
1.2893796958227628