# scipy.stats.mstats.sem

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

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

  Also sometimes called standard error of measurement.

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

  Examples

  Find standard error along the first axis:

      >>> 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

scipy.stats.mstats.find_repeats scipy.stats.mstats.trimmed_mean