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Module « scipy.stats »

Fonction shapiro - module scipy.stats

Signature de la fonction shapiro

def shapiro(x, *, axis=None, nan_policy='propagate', keepdims=False) 

Description

help(scipy.stats.shapiro)

    


Perform the Shapiro-Wilk test for normality.

The Shapiro-Wilk test tests the null hypothesis that the
data was drawn from a normal distribution.

Parameters
----------
x : array_like
    Array of sample data. Must contain at least three observations.
axis : int or None, default: None
    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
    The test statistic.
p-value : float
    The p-value for the hypothesis test.

See Also
--------

:func:`anderson`
    The Anderson-Darling test for normality
:func:`kstest`
    The Kolmogorov-Smirnov test for goodness of fit.
:ref:`hypothesis_shapiro`
    Extended example


Notes
-----
The algorithm used is described in [4]_ but censoring parameters as
described are not implemented. For N > 5000 the W test statistic is
accurate, but the p-value may not be.

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] https://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
       :doi:`10.18434/M32189`
.. [2] Shapiro, S. S. & Wilk, M.B, "An analysis of variance test for
       normality (complete samples)", Biometrika, 1965, Vol. 52,
       pp. 591-611, :doi:`10.2307/2333709`
.. [3] Razali, N. M. & Wah, Y. B., "Power comparisons of Shapiro-Wilk,
       Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests", Journal
       of Statistical Modeling and Analytics, 2011, Vol. 2, pp. 21-33.
.. [4] Royston P., "Remark AS R94: A Remark on Algorithm AS 181: The
       W-test for Normality", 1995, Applied Statistics, Vol. 44,
       :doi:`10.2307/2986146`

Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> rng = np.random.default_rng()
>>> x = stats.norm.rvs(loc=5, scale=3, size=100, random_state=rng)
>>> shapiro_test = stats.shapiro(x)
>>> shapiro_test
ShapiroResult(statistic=0.9813305735588074, pvalue=0.16855233907699585)
>>> shapiro_test.statistic
0.9813305735588074
>>> shapiro_test.pvalue
0.16855233907699585

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

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