For a normal distribution, critical values for this statistic have been determined by simulation. The original tables were computed by Filliben and more extensive versions of the tables were computed by Devaney.
The current tables are available for N = 3 to 1,000 and for signficance levels of 0.01 or 0.05 (the PPCC provides a lower tailed test). Significance levels of 0.99 and 0.95 are interpreted as 0.01 and 0.05, respectively.
PPCC values less than the critical value reject the hypothesis of a normal distribution. Syntax:
where <n> is a variable, parameter or number indicating the sample size;
and <k> is a variable, parameter or number indicating the significance level;
<y> is a variable or a parameter (depending on what <n> and <k> are) where the computed critical value is stored.
LET A = NORPPCV(N,0.01)
LET A = NORPPCV(N,0.05)
LET A = NORPPCV(105,0.05)
Judy Devaney, Phd Thesis, George Mason University.
. Step 1: Read the data . skip 25 read zarr13.dat y . . Step 2: Generate normal probability plot with PPCC . value and associated critical values . let n = size y let alpha = 0.01 let cv1 = norppcv(n,alpha) let cv1 = round(cv1,3) let alpha = 0.05 let cv2 = norppcv(n,alpha) let cv2 = round(cv2,3) . char circle char fill on char hw 0.5 0.375 line blank y1label Sorted Data x1label Percentiles of Normal Distribution title Normal Probability Plot for ZARR13.DAT title case asis title offset 2 label case asis ylimits 9.1 9.4 major y1tic mark number 4 . normal probability plot y . let ppcc = round(ppcc,3) let ppa0 = round(ppa0,3) let ppa1 = round(ppa1,3) case asis justification left move 16 88 text Location: ^ppa0 move 16 85 text Scale: ^ppa1 move 16 82 text PPCC: ^ppcc move 16 79 text 0.01 CV: ^cv1 move 16 76 text 0.05 CV: ^cv2