scipy.stats.jarque_bera¶
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scipy.stats.
jarque_bera
(x)[source]¶ 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.
Returns: jb_value : float
The test statistic.
p : float
The p-value for the hypothesis test.
References
[R653] Jarque, C. and Bera, A. (1980) “Efficient tests for normality, homoscedasticity and serial independence of regression residuals”, 6 Econometric Letters 255-259. Examples
>>> from scipy import stats >>> np.random.seed(987654321) >>> x = np.random.normal(0, 1, 100000) >>> y = np.random.rayleigh(1, 100000) >>> stats.jarque_bera(x) (4.7165707989581342, 0.09458225503041906) >>> stats.jarque_bera(y) (6713.7098548143422, 0.0)