# scipy.stats.sem

scipy.stats.sem(a, axis=0, ddof=1, nan_policy='propagate')

  Compute standard error of the mean.

  Calculate 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

      Axis along which to operate. Default is 0. If None, compute over the whole array a.

    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.

    nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional

      Defines how to handle when input contains nan. The following options are available (default is ‘propagate’):

      > - ‘propagate’: returns nan

      > - ‘raise’: throws an error

      > - ‘omit’: performs the calculations ignoring nan values

  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 and np.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

scipy.stats.iqr scipy.stats.bayes_mvs