Showing posts with the label kolmogorov-smirnov-test-python-visualization

Parametric Techniques on Other Distributions- THE KOLMOGOROV-SMIRNOV (K-S) TEST

Parametric Techniques on Other Distributions THE KOLMOGOROV-SMIRNOV (K-S) TEST The chi-square test is no doubt the most popular of all methods of  comparing two distributions. Since many market-oriented applications  other than the ones . However, the best test for our pur poses may well be the K-S test. This very efficient test is applicable to  unbinned distributions that are a function of a single independent vari able.  All cumulative density functions have a minimum value of 0 and a  maximum value of 1. What goes on in between differentiates them. The  K-S test measures a very simple variable, D, which is defined as the  maximum absolute value of the difference between two distributions'  cumulative density functions.  To perform the K-S test is relatively simple. N objects are standardized  and sorted in ascending order.  As we go through  these sorted and standardized trades, the cumulative probability is how ever many trades we've gone through divided b