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Module « scipy.stats »

Fonction kurtosistest - module scipy.stats

Signature de la fonction kurtosistest

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

Description

help(scipy.stats.kurtosistest)

    


Test whether a dataset has normal kurtosis.

This function tests the null hypothesis that the kurtosis
of the population from which the sample was drawn is that
of the normal distribution.

Parameters
----------
a : array
    Array of the sample data. Must contain at least five 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.
    The following options are available (default is 'two-sided'):
    
    * 'two-sided': the kurtosis of the distribution underlying the sample
      is different from that of the normal distribution
    * 'less': the kurtosis of the distribution underlying the sample
      is less than that of the normal distribution
    * 'greater': the kurtosis 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_kurtosistest`
    Extended example


Notes
-----
Valid only for n>20. This function uses the method described in [1]_.

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] F. J. Anscombe, W. J. Glynn, "Distribution of the kurtosis
   statistic b2 for normal samples", Biometrika, vol. 70, pp. 227-234, 1983.

Examples
--------
>>> import numpy as np
>>> from scipy.stats import kurtosistest
>>> kurtosistest(list(range(20)))
KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.08804338332528348)
>>> kurtosistest(list(range(20)), alternative='less')
KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.04402169166264174)
>>> kurtosistest(list(range(20)), alternative='greater')
KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.9559783083373583)
>>> rng = np.random.default_rng()
>>> s = rng.normal(0, 1, 1000)
>>> kurtosistest(s)
KurtosistestResult(statistic=-1.475047944490622, pvalue=0.14019965402996987)

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

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