scipy.stats.jarque_bera#
- 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
- 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