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
```