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.semas 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