scipy.stats.mstats.

sem#

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

Calculates the standard error of the mean of the input array.

Also sometimes called standard error of measurement.

Parameters:

aarray_like: An array containing the values for which the standard error is returned.
axisint 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.
ddofint, 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:

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

Notes

The default value for ddof changed in scipy 0.15.0 to be consistent with scipy.stats.sem as well as with the most common definition used (like in the R documentation).

Examples

Find standard error along the first axis:

>>> import numpy as np
>>> from scipy import stats
>>> a = np.arange(20).reshape(5,4)
>>> print(stats.mstats.sem(a))
[2.8284271247461903 2.8284271247461903 2.8284271247461903
 2.8284271247461903]

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

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