What type of variables does Pearson's correlation coefficient evaluate?

Pearson's correlation coefficient evaluates bivariate variables, which are two continuous variables that can be analyzed to determine the strength and direction of their linear relationship. This statistical measure is significant for understanding how changes in one variable are associated with changes in another variable. For instance, if you were to investigate the relationship between height and weight, both of these attributes would be measured on a continuous scale, thus making them suitable for Pearson's correlation analysis.

The focus of Pearson's correlation coefficient is specifically on pairs of numerical data points, which allows for a direct comparison of their linear relationship. This approach is not applicable for nominal variables, which categorize data without any quantitative significance, nor for ordinal variables, where the order is meaningful but the distance between values may not be uniform. Lastly, multivariate variables involve more than two variables, which goes beyond the scope of what Pearson's coefficient evaluates, as it is designed to assess only two variables at a time.