The correlation coefficient is a measure of the strength and direction of the relationship between two variables and can take on any values between 1.0 and +1.0; that is, the correlation coefficient can be decomposed into its direction or sign (positive or negative relationship between two variables) and the magnitude or strength of the relationship (the higher the absolute value of the correlation coefficient, the stronger the relationship).

The correlation coefficient can be computed in several ways. The first approach is to manually compute the correlation coefficient r of a pair of variables x and y using:

The second approach is to use Excel s CORREL function. For instance, if the 10 data points for x and y are listed in cells A1 : B10, then the Excel function to use is CORREL (A1 : A10, B1 : B10).

The third approach is to run Risk Simulator s Multi-Variable Distributional Fitting Tool, and the resulting correlation matrix will be computed and displayed.

Correlation does not imply causation. Two completely unrelated random variables might display some correlation, but this does not imply any causation between the two (e.g., sunspot activity and events in the stock market are correlated, but there is no causation between the two).

There are two general types of correlations: parametric and nonparametric correlations. Pearson s correlation coefficient is the most common correlation measure and is usually referred to simply as the correlation coefficient. However, Pearson s correlation is a...