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Module « scipy.stats »

Fonction skewtest - module scipy.stats

Signature de la fonction skewtest

def skewtest(a, axis=0, nan_policy='propagate', alternative='two-sided', *, keepdims=False) 

Description

help(scipy.stats.skewtest)

    


Test whether the skew is different from the normal distribution.

This function tests the null hypothesis that the skewness of
the population that the sample was drawn from is the same
as that of a corresponding normal distribution.

Parameters
----------
a : array
    The data 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.
alternative : {'two-sided', 'less', 'greater'}, optional
    Defines the alternative hypothesis. Default is 'two-sided'.
    The following options are available:
    
    * 'two-sided': the skewness of the distribution underlying the sample
      is different from that of the normal distribution (i.e. 0)
    * 'less': the skewness of the distribution underlying the sample
      is less than that of the normal distribution
    * 'greater': the skewness of the distribution underlying the sample
      is greater than that of the normal distribution
    
    .. versionadded:: 1.7.0
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 computed z-score for this test.
pvalue : float
    The p-value for the hypothesis test.

See Also
--------

:ref:`hypothesis_skewtest`
    Extended example


Notes
-----
The sample size must be at least 8.

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] R. B. D'Agostino, A. J. Belanger and R. B. D'Agostino Jr.,
        "A suggestion for using powerful and informative tests of
        normality", American Statistician 44, pp. 316-321, 1990.

Examples
--------
>>> from scipy.stats import skewtest
>>> skewtest([1, 2, 3, 4, 5, 6, 7, 8])
SkewtestResult(statistic=1.0108048609177787, pvalue=0.3121098361421897)
>>> skewtest([2, 8, 0, 4, 1, 9, 9, 0])
SkewtestResult(statistic=0.44626385374196975, pvalue=0.6554066631275459)
>>> skewtest([1, 2, 3, 4, 5, 6, 7, 8000])
SkewtestResult(statistic=3.571773510360407, pvalue=0.0003545719905823133)
>>> skewtest([100, 100, 100, 100, 100, 100, 100, 101])
SkewtestResult(statistic=3.5717766638478072, pvalue=0.000354567720281634)
>>> skewtest([1, 2, 3, 4, 5, 6, 7, 8], alternative='less')
SkewtestResult(statistic=1.0108048609177787, pvalue=0.8439450819289052)
>>> skewtest([1, 2, 3, 4, 5, 6, 7, 8], alternative='greater')
SkewtestResult(statistic=1.0108048609177787, pvalue=0.15605491807109484)

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

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