If we conducted a competition for which statistical quantity would be the most valuable in exploratory data analysis, the winner would most likely be the correlation coefficient with a significant difference from its first competitor. In addition, most data applications contain non-normal data...

We reformulate and decompose the Pearson and Spearman correlation coefficients into two components. We recommend the first component for detecting linear or monotonic relationships and the second for recognizing patterns of two parallel lines, providing robust versions to outliers. Thus, we...

We decompose the Pearson correlation coefficient into two components. We recommend the first component for detecting linear relationships and the second for recognizing patterns of two parallel lines, providing robust versions to outliers. The significance of the Pearson coefficient without...