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# statsmodels.expected_robust_kurtosis() in Python

• Last Updated : 10 May, 2020

With the help of `statsmodels.expected_robust_kurtosis()` method, we can calculate the expected value of robust kurtosis measure by using `statsmodels.expected_robust_kurtosis()` method. Syntax : `statsmodels.expected_robust_kurtosis(ab, db)`

Return : Return the four kurtosis value i.e kr1, kr2, kr3 and kr4.

Example #1 :
In this example we can see that by using `statsmodels.expected_robust_kurtosis()` method, we are able to get the expected value of robust kurtosis measure by using this method.

 `# import numpy and statsmodels``import` `numpy as np``from` `statsmodels.stats.stattools ``import` `expected_robust_kurtosis``   ` `# Using statsmodels.expected_robust_kurtosis() method``gfg ``=` `expected_robust_kurtosis()``   ` `print``(gfg)`

Output :

[3.0000000 1.23309512 2.58522712 2.90584695]

Example #2 :

 `# import numpy and statsmodels``import` `numpy as np``from` `statsmodels.stats.stattools ``import` `expected_robust_kurtosis``   ` `# Using statsmodels.expected_robust_kurtosis() method``gfg ``=` `expected_robust_kurtosis([``12``, ``22``], [``6``, ``7``])``   ` `print``(gfg)`

Output :

[3.0000000 1.23309512 1.23859789 1.0535188 ]

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