Differences between distributions of variables between groups were analyzed by Fishers exact text.
A breakdown of associations between variables to linear correlations between pairs of variables revealed only two significant correlations among the environmental variables measured.
In a GGM, regulations between genes are estimated by calculating the correlation between pairs of variables.
The differences in the magnitude of inequalities between groups of variables suggest different pathways might be operating between SEP and each of the groups.
Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters.
This indicated that the correlations between pairs of variables can be explained by the other variables.
A test for interaction between pairs of variables in the final model was performed.
These explanations are useful for finding relationships between groups of variables and treatments.
The correlation coefficient matrix quantifies the linear relationship existing between pairs of variables present therein.
The separability is estimated by computing the Pearson correlation indices between pairs of variables.
