The first report is just a table of the Pearson correlation coefficients among the variables you specified. But correlations are more interesting if you can see them visually. JMP produces a scatterplot matrix showing scatterplots for each pair of variables . You can brush in one plot, and all the corresponding points highlight in the other plots. The normal-density ellipse shows the correlation visually.
High correlations correspond to ellipses that are narrow across the diagonal.
Small correlations lead to more circular ellipses. Negative correlations show up as ellipses with a negative slope. These correlations can be used to help measure each point's outlyingness with respect to the correlation structure in a high number of dimensions. Visualize a series of nested ellipses corresponding to many normal density contours. The number of ellipses you cross is a measure of outlyingness called the Mahalanobis distance.