scipy.stats.jarque_bera

scipy.stats.jarque_bera(x)

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

xarray_like

Observations of a random variable.

Returns

jb_valuefloat

The test statistic.

pfloat

The p-value for the hypothesis test.

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

>>> 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