Both the correlation coefficient and the covariance are measures of the extent to which two variables “vary together.” Corresponding covariances are not scaled. The difference is that correlation coefficients are scaled to lie between -1 and +1 inclusive. The Correlation and Covariance tools each give an output table, a matrix, that shows the correlation coefficient or covariance, respectively, between each pair of measurement variables.
The Correlation and Covariance tools can both be used in the same setting, when you have N different measurement variables observed on a set of individuals. (For example, if the two measurement variables are weight and height, the value of the correlation coefficient is unchanged if weight is converted from pounds to kilograms.) The value of any correlation coefficient must be between -1 and +1 inclusive. The correlation coefficient, like the covariance, is a measure of the extent to which two measurement variables “vary together.” Unlike the covariance, the correlation coefficient is scaled so that its value is independent of the units in which the two measurement variables are expressed. It provides an output table, a correlation matrix, that shows the value of CORREL (or PEARSON) applied to each possible pair of measurement variables. (Any missing observation for any subject causes that subject to be ignored in the analysis.) The Correlation analysis tool is particularly useful when there are more than two measurement variables for each of N subjects. The CORREL and PEARSON worksheet functions both calculate the correlation coefficient between two measurement variables when measurements on each variable are observed for each of N subjects.