Module « scipy.stats »

Fonction kurtosistest - module scipy.stats

Signature de la fonction kurtosistest

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

Description

kurtosistest.__doc__

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.
    axis : int or None, optional
       Axis along which to compute test. Default is 0. If None,
       compute over the whole array `a`.
    nan_policy : {'propagate', 'raise', 'omit'}, optional
        Defines how to handle when input contains nan.
        The following options are available (default is 'propagate'):

        * 'propagate': returns nan
        * 'raise': throws an error
        * 'omit': performs the calculations ignoring nan values

    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

    Returns
    -------
    statistic : float
        The computed z-score for this test.
    pvalue : float
        The p-value for the hypothesis test.

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

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

    Examples
    --------
    >>> 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)