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Module « scipy.stats »

Fonction jarque_bera - module scipy.stats

Signature de la fonction jarque_bera

def jarque_bera(x, *, axis=None, nan_policy='propagate', keepdims=False) 

Description

help(scipy.stats.jarque_bera)

    


Perform the Jarque-Bera goodness of fit test on sample data.

The Jarque-Bera test tests whether the sample data has the skewness and
kurtosis matching a normal distribution.

Note that this test only works for a large enough number of data samples
(>2000) as the test statistic asymptotically has a Chi-squared distribution
with 2 degrees of freedom.

Parameters
----------
x : array_like
    Observations of a random variable.
axis : int or None, default: None
    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.
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
-------
result : SignificanceResult
    An object with the following attributes:
    
    statistic : float
        The test statistic.
    pvalue : float
        The p-value for the hypothesis test.

See Also
--------

:ref:`hypothesis_jarque_bera`
    Extended example


Notes
-----

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] Jarque, C. and Bera, A. (1980) "Efficient tests for normality,
       homoscedasticity and serial independence of regression residuals",
       6 Econometric Letters 255-259.

Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> rng = np.random.default_rng()
>>> x = rng.normal(0, 1, 100000)
>>> jarque_bera_test = stats.jarque_bera(x)
>>> jarque_bera_test
Jarque_beraResult(statistic=3.3415184718131554, pvalue=0.18810419594996775)
>>> jarque_bera_test.statistic
3.3415184718131554
>>> jarque_bera_test.pvalue
0.18810419594996775

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

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