Statistical Functions

the statistical functions processor can help you determine how well a set of data matches a certain distribution, especially normal distribution you can do this by analyzing the data's characteristics and calculating corresponding p values statistical functions overview jarque bera test this is simple goodness of fit test to check if the sample data has similar skewness and kurtosis to a normal distribution the further the jb test value is from zero, the stronger the fact that the data does not belong to the normal distribution skewness and kurtosis are calculated from the window of data and then jb value is calculated jb = (n/6) (skewness^2 + (1/4)(kurtosis 3)^2) p value is calculated by subtracting cumulative distribution function at the jb value for the chisquared distribution with 1 cramer von mises test this is goodness of fit test which uses the summed squared differences between the sample of data, and the expected cumulative distribution function first, a window of data is collected, then mean and standard deviation is calculated data is then sorted, and z scores are calculated by standardizing the data cramervonmises = (1/12n) + sigma { ((2i 1)/(2n) phiz)^2 } p value is calculated, based on the value of cramervonmises anderson darling test this is a statistical test to check whether a given sample of data is from an empirical distribution function or not in this case, we are using a normal distribution meaning, we use this test to see how far the data departs from an ideal normal distribution compared to other tests, anderson darling test gives more weight to the tails of the distribution first, a window of data is collected, then mean and standard deviation is calculated data is then sorted, and z scores are calculated by standardizing the data a^2 = n (1/n) sigma{ (2i 1)ln(phiz\[i]) + (2(n i)+1)ln(phz\[i]) } formula is applied, where phiz is the cumulative distribution function for the z scores p value is calculated, based on the value of a^2 d'agostino pearson test with a combination of skewness and kurtosis test, d'agostino pearson test checks whether the shape of the window of values matches a normal distributions kolmogorov smirnov test lilliefors test is use to check the hypothesis of normality for the kolmogorov smirnov test first, a window of data is collected, then mean and standard deviation is calculated d+ and d are calculated by taking maximum discrepancy between the empirical distribution function and the cumulative distribution function k statistic is just max(d+,d ) sqrt(n) statistical functions parameters parameters details window size this parameter represents the window in which the calculations will be performed jarque bera anderson darling cramer von mises d agostino pearson kolmogorov smirnov select the test you want the processor to calculate note when creating an analytics flow with statistical functions processor, refer the use the statistical prediction function docid\ w6e7xmspj jgcgj278qn7 guide for more details