Module « scipy.stats »

Fonction shapiro - module scipy.stats

Signature de la fonction shapiro

def shapiro(x) 

Description

shapiro.__doc__

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.

    Returns
    -------
    statistic : float
        The test statistic.
    p-value : float
        The p-value for the hypothesis test.

    See Also
    --------
    anderson : The Anderson-Darling test for normality
    kstest : The Kolmogorov-Smirnov test for goodness of fit.

    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.

    The chance of rejecting the null hypothesis when it is true is close to 5%
    regardless of sample size.

    References
    ----------
    .. [1] https://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
    .. [2] Shapiro, S. S. & Wilk, M.B (1965). An analysis of variance test for
           normality (complete samples), Biometrika, Vol. 52, pp. 591-611.
    .. [3] Razali, N. M. & Wah, Y. B. (2011) Power comparisons of Shapiro-Wilk,
           Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests, Journal of
           Statistical Modeling and Analytics, Vol. 2, pp. 21-33.
    .. [4] ALGORITHM AS R94 APPL. STATIST. (1995) VOL. 44, NO. 4.

    Examples
    --------
    >>> 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