dask.array.stats.kurtosistest
dask.array.stats.kurtosistest¶
- dask.array.stats.kurtosistest(a, axis=0, nan_policy='propagate')[source]¶
Test whether a dataset has normal kurtosis.
This docstring was copied from scipy.stats.kurtosistest.
Some inconsistencies with the Dask version may exist.
This function tests the null hypothesis that the kurtosis of the population from which the sample was drawn is that of the normal distribution.
- Parameters
- aarray
Array of the sample data.
- axisint or None, optional
Axis along which to compute test. Default is 0. If None, compute over the whole array a.
- nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional
Defines how to handle when input contains nan. The following options are available (default is ‘propagate’):
‘propagate’: returns nan
‘raise’: throws an error
‘omit’: performs the calculations ignoring nan values
- alternative{‘two-sided’, ‘less’, ‘greater’}, optional (Not supported in Dask)
Defines the alternative hypothesis. The following options are available (default is ‘two-sided’):
‘two-sided’: the kurtosis of the distribution underlying the sample is different from that of the normal distribution
‘less’: the kurtosis of the distribution underlying the sample is less than that of the normal distribution
‘greater’: the kurtosis of the distribution underlying the sample is greater than that of the normal distribution
New in version 1.7.0.
- Returns
- statisticfloat
The computed z-score for this test.
- pvaluefloat
The p-value for the hypothesis test.
Notes
Valid only for n>20. This function uses the method described in [1].
References
- 1
see e.g. F. J. Anscombe, W. J. Glynn, “Distribution of the kurtosis statistic b2 for normal samples”, Biometrika, vol. 70, pp. 227-234, 1983.
Examples
>>> from scipy.stats import kurtosistest >>> kurtosistest(list(range(20))) KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.08804338332528348) >>> kurtosistest(list(range(20)), alternative='less') KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.04402169166264174) >>> kurtosistest(list(range(20)), alternative='greater') KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.9559783083373583)
>>> rng = np.random.default_rng() >>> s = rng.normal(0, 1, 1000) >>> kurtosistest(s) KurtosistestResult(statistic=-1.475047944490622, pvalue=0.14019965402996987)