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Module « scipy.stats »

Fonction normaltest - module scipy.stats

Signature de la fonction normaltest

def normaltest(a, axis=0, nan_policy='propagate', *, keepdims=False) 

Description

help(scipy.stats.normaltest)

    


Test whether a sample differs from a normal distribution.

This function tests the null hypothesis that a sample comes
from a normal distribution.  It is based on D'Agostino and
Pearson's [1]_, [2]_ test that combines skew and kurtosis to
produce an omnibus test of normality.

Parameters
----------
a : array_like
    The array containing the sample to be tested. Must contain
    at least eight 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.
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
-------
statistic : float or array
    ``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
    ``k`` is the z-score returned by `kurtosistest`.
pvalue : float or array
    A 2-sided chi squared probability for the hypothesis test.

See Also
--------

:ref:`hypothesis_normaltest`
    Extended example


Notes
-----

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``.

References
----------
.. [1] D'Agostino, R. B. (1971), "An omnibus test of normality for
        moderate and large sample size", Biometrika, 58, 341-348
.. [2] D'Agostino, R. and Pearson, E. S. (1973), "Tests for departure from
        normality", Biometrika, 60, 613-622

Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> rng = np.random.default_rng()
>>> pts = 1000
>>> a = rng.normal(0, 1, size=pts)
>>> b = rng.normal(2, 1, size=pts)
>>> x = np.concatenate((a, b))
>>> res = stats.normaltest(x)
>>> res.statistic
53.619...  # random
>>> res.pvalue
2.273917413209226e-12  # random

For a more detailed example, see :ref:`hypothesis_normaltest`.

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