# Pearson Correlation

The most widely-used type of correlation coefficient is Pearson r (Pearson, 1896), also called linear or product-moment correlation (the term correlation was first used by Galton, 1888). It is the basic type of correlation that is offered in the Basic Statistics and Tables module; the Pearson Product Moment Correlation Distribution option is also available from the Corr option of the Statistics - Probability Calculator menu. Using non-technical language, we could say that the correlation coefficient determines the extent to which values of two variables are "proportional" to each other. The value of the correlation (i.e., correlation coefficient) does not depend on the specific measurement units used; for example, the correlation between height and weight will be identical regardless of whether inches and pounds, or centimeters and kilograms are used as measurement units. Proportional means linearly related; that is, the correlation is high if it can be approximated by a straight line (sloped upwards or downwards). This line is called the regression line or least squares line, because it is determined such that the sum of the squared distances of all the data points from the line is the lowest possible. Pearson correlation assumes that the two variables are measured on at least interval scales. The Pearson product moment correlation coefficient is calculated as follows:

r12 = S(Yi1 - Y-bar1)*(Yi2 - Y-bar2) / [S(Yi1 - Y-bar1)2*S(Yi2 - Y-bar2)2](1/2)