A number, mathematically represented by "r," that is a quantification of the linear relationship that exists between two sets of paired data or two random variables. It is equal to their covariance divided by the product of their standard deviations and has a two-decimal value ranging between -1.00 and +1.00. The nearer the number is to 1.00 (plus or minus), the stronger is the correlation (either positive or negative). See text, Chapter 13. See also, "correlated," "mean," "predictive reliability," "regression" and "standard deviation."

1. a statistic that assesses the degree of association between two or more variables. 2. A number that represents the direction and strength of a correlation. 54, 649

a statistic representing how closely two variables co-vary; it can vary from -1 (perfect negative correlation) through 0 (no correlation) to +1 (perfect positive correlation); "what is the correlation between those two variables?"

A measure of the extent to which the values of variables in a sample or a population are interdependent.

A measure of the relationship (Correlation) between two variables. The coefficient ranges from plus one when a perfect positive correlation exists (as X increases, Y increases linearly) to a negative one when there is a perfect negative correlation. A correlation coefficient of zero indicates no relationship between the variables. See Coefficient of Determination and Regression Analysis. www.sceaonline.net Keyword(s): Coefficient of Correlation

A second measure of the strength of the regression relationship, which tells us the degree of linear association between x and y. (For more information, see the Introduction to Statistics Using Excel Workbook .)

In regression analysis this is a statistic (designated as r and ranging from -1 to +1) indicating the percentage of correlation between the dependent variable and the independent variable(s). When this statistic is squared it produces the coefficient of determination, which indicates the percentage change in the dependent variable that is explained by the change in the independent variable(s). (Correlation does not necessarily mean there is a cause-and-effect relationship. To Top