Module « scipy.stats »

Fonction anderson - module scipy.stats

Signature de la fonction anderson

def anderson(x, dist='norm') 

Description

anderson.__doc__

Anderson-Darling test for data coming from a particular distribution.

    The Anderson-Darling test tests the null hypothesis that a sample is
    drawn from a population that follows a particular distribution.
    For the Anderson-Darling test, the critical values depend on
    which distribution is being tested against.  This function works
    for normal, exponential, logistic, or Gumbel (Extreme Value
    Type I) distributions.

    Parameters
    ----------
    x : array_like
        Array of sample data.
    dist : {'norm', 'expon', 'logistic', 'gumbel', 'gumbel_l', 'gumbel_r', 'extreme1'}, optional
        The type of distribution to test against.  The default is 'norm'.
        The names 'extreme1', 'gumbel_l' and 'gumbel' are synonyms for the
        same distribution.

    Returns
    -------
    statistic : float
        The Anderson-Darling test statistic.
    critical_values : list
        The critical values for this distribution.
    significance_level : list
        The significance levels for the corresponding critical values
        in percents.  The function returns critical values for a
        differing set of significance levels depending on the
        distribution that is being tested against.

    See Also
    --------
    kstest : The Kolmogorov-Smirnov test for goodness-of-fit.

    Notes
    -----
    Critical values provided are for the following significance levels:

    normal/exponential
        15%, 10%, 5%, 2.5%, 1%
    logistic
        25%, 10%, 5%, 2.5%, 1%, 0.5%
    Gumbel
        25%, 10%, 5%, 2.5%, 1%

    If the returned statistic is larger than these critical values then
    for the corresponding significance level, the null hypothesis that
    the data come from the chosen distribution can be rejected.
    The returned statistic is referred to as 'A2' in the references.

    References
    ----------
    .. [1] https://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
    .. [2] Stephens, M. A. (1974). EDF Statistics for Goodness of Fit and
           Some Comparisons, Journal of the American Statistical Association,
           Vol. 69, pp. 730-737.
    .. [3] Stephens, M. A. (1976). Asymptotic Results for Goodness-of-Fit
           Statistics with Unknown Parameters, Annals of Statistics, Vol. 4,
           pp. 357-369.
    .. [4] Stephens, M. A. (1977). Goodness of Fit for the Extreme Value
           Distribution, Biometrika, Vol. 64, pp. 583-588.
    .. [5] Stephens, M. A. (1977). Goodness of Fit with Special Reference
           to Tests for Exponentiality , Technical Report No. 262,
           Department of Statistics, Stanford University, Stanford, CA.
    .. [6] Stephens, M. A. (1979). Tests of Fit for the Logistic Distribution
           Based on the Empirical Distribution Function, Biometrika, Vol. 66,
           pp. 591-595.