Computing and graphing probability values of pearson distributions: a SAS/IML macro

摘要

Most of statistical analysis relies on normal distributions, but this assumption is often difficult to meet in reality. Pearson distributions can be approximated for any data using the first four moments of the data [ – ]. Thus, Pearson distributions made statistical analysis possible for any data with unknown distributions. For instance, in hypothesis testing, a sampling distribution of an observed test statistic is usually unknown but the sampling distribution can be fitted into one of Pearson distributions. Then, we can compute and use a -value (or probability value) of the approximated Pearson distribution to make a statistical decision for such distribution-free hypothesis testing.