  Any empirical data can be approximated to one of Pearson distributions using
the first four moments of the data (Elderton and Johnson, 1969; Pearson, 1895;
Solomon and Stephens, 1978). Thus, Pearson distributions made statistical
analysis possible for data with unknown distributions. There are both extant
old-fashioned in-print tables (Pearson and Hartley, 1972) and contemporary
computer programs (Amos and Daniel, 1971; Bouver and Bargmann, 1974; Bowman and
Shenton, 1979; Davis and Stephens, 1983; Pan, 2009) available for obtaining
percentage points of Pearson distributions corresponding to certain
pre-specifed percentages (or probability values) (e.g., 1.0%, 2.5%, 5.0%,
etc.), but they are little useful in statistical analysis because we have to
rely on unwieldy second difference interpolation to calculate a probability
value of a Pearson distribution corresponding to any given percentage point,
such as an observed test statistic in hypothesis testing. Thus, the present
study develops a SAS/IML macro program to compute and graph probability values
of Pearson distributions for any given percentage point so as to facilitate
researchers to conduct statistical analysis on data with unknown distributions.
