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Tag Archives: Python-statsmodels

To understand what power analysis is, we must first take a look at the concepts of a statistical hypothesis test. A statistical hypothesis test calculates… Read More
With the help of statsmodels.robust_kurtosis() method, we can calculate the four kurtosis value by using statsmodels.robust_kurtosis() method. Syntax : statsmodels.robust_kurtosis(numpy_array) Return : Return four value… Read More
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… Read More
With the help of statsmodels.robust_skewness() method, we can calculate the four skewness measures in Kim & White. Syntax : statsmodels.robust_skewness(array, axis) Return : Return the… Read More
With the help of statsmodels.medcouple() method, we can calculate the medcouple robust measure of skew. Syntax : statsmodels.medcouple(array, axis) Return : Return the medcouple statistic… Read More
With the help of statsmodels.omni_normtest() method, we can get the omnibus test for normality and we use chi^2 score for this statsmodels.omni_normtest() method. Syntax :… Read More
With the help of statsmodels.jarque_bera() method, we can get the jarque bera test for normality and it’s a test based on skewness, and the kurtosis,… Read More
With the help of statsmodels.durbin_watson() method, we can get the durbin watson test statistics and it is equal to 2*(1-r), where r is autocorrelation between… Read More