I would really look at some of the procedures in SAS/ETS, or at some mixed modeling techniques that address autocorrelation, for timeseries comparisons.  The usual methods (TTEST, GLM) offered for analysis fail horribly on the assumption of independence of the observations.  I would recommend PROC MIXED with the REPEATED statement, or PROC PANEL in SAS/ETS. Steve Denham ... View more

PROBT is an older function. Use the CDF function instead, like this: prob = 1 - CDF("T", x, df); For reasos why you should use CDF instead of PROBT, see reasons 4-6 of this article: Six reasons you should stop using the RANUNI function to generate random numbers - The DO Loop ... View more

Maybe I'm missing something, but I think there is an easier way. Turn on the FISHER option and use ODS output to save the FisherPearsonCorr table. The FisherPearsonCorr table is stored in "long" format instead of "wide" format. Thus it is trivial to count the significant p-values and to know which are associate with positive and negative correlations. No arrays required. proc corr data=sashelp.cars outp=out fisher;    ods output FisherPearsonCorr=fisher; run; proc print data=Fisher(obs=5); var Var WithVar Corr pValue; run; ... View more