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Module « scipy.stats »

Fonction sem - module scipy.stats

Signature de la fonction sem

def sem(a, axis=0, ddof=1, nan_policy='propagate', *, keepdims=False) 

Description

help(scipy.stats.sem)

    


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. Must contain at least two observations.
axis : int or None, default: 0
    If an int, the axis of the input along which to compute the statistic.
    The statistic of each axis-slice (e.g. row) of the input will appear in a
    corresponding element of the output.
    If ``None``, the input will be raveled before computing the statistic.
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', 'omit', 'raise'}
    Defines how to handle input NaNs.
    
    - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
      which the  statistic is computed, the corresponding entry of the output
      will be NaN.
    - ``omit``: NaNs will be omitted when performing the calculation.
      If insufficient data remains in the axis slice along which the
      statistic is computed, the corresponding entry of the output will be
      NaN.
    - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
keepdims : bool, default: False
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

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.

Beginning in SciPy 1.9, ``np.matrix`` inputs (not recommended for new
code) are converted to ``np.ndarray`` before the calculation is performed. In
this case, the output will be a scalar or ``np.ndarray`` of appropriate shape
rather than a 2D ``np.matrix``. Similarly, while masked elements of masked
arrays are ignored, the output will be a scalar or ``np.ndarray`` rather than a
masked array with ``mask=False``.

Examples
--------
Find standard error along the first axis:

>>> import numpy as np
>>> 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

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