Proc corr can perform Fisher’s Z transformation to compare correlations. This makes performing hypothesis test on Pearson correlation coefficients much easier. The only thing that one has to do is to add option fisher to the proc corr statement.
Example 1. Testing on correlation = 0.
proc corr data = hsb2 fisher; var write math; run;2 Variables: write math Simple Statistics Variable N Mean Std Dev Sum Minimum Maximum write 200 52.77500 9.47859 10555 31.00000 67.00000 math 200 52.64500 9.36845 10529 33.00000 75.00000 Pearson Correlation Coefficients, N = 200 Prob > |r| under H0: Rho=0 write math write 1.00000 0.61745 <.0001 math 0.61745 1.00000 <.0001 Pearson Correlation Statistics (Fisher's z Transformation) With Sample Bias Correlation Variable Variable N Correlation Fisher's z Adjustment Estimate write math 200 0.61745 0.72087 0.00155 0.61649 Pearson Correlation Statistics (Fisher's z Transformation) With p Value for Variable Variable 95% Confidence Limits H0:Rho=0 write math 0.522432 0.695723 <.0001
Example 2. Testing on correlation = specific value.
proc corr data = hsb2 fisher (rho0=.2) nosimple; var write math; run;2 Variables: WRITE MATHPearson Correlation Coefficients, N = 200 Prob > |r| under H0: Rho=0WRITE MATHWRITE 1.00000 0.61745 writing score <.0001MATH 0.61745 1.00000 math score <.0001Pearson Correlation Statistics (Fisher's z Transformation)With Sample Bias Correlation Variable Variable N Correlation Fisher's z Adjustment EstimateWRITE MATH 200 0.61745 0.72087 0.00155 0.61649Pearson Correlation Statistics (Fisher's z Transformation)With ------H0:Rho=Rho0----- Variable Variable 95% Confidence Limits Rho0 p ValueWRITE MATH 0.522432 0.695723 0.20000 <.0001