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When two variables vary together, statisticians say that there is a lot of covariation or correlation. The correlation coefficient, r, quantifies the direction and magnitude of correlation. Correlation is not the same as linear regression, but the two are related. Linear regression finds the line that best predicts Y from X. Correlation quantifies how well X and Y vary together. In some situations, you might want to perform both calculations.Correlation only makes sense when both X and Y variables are outcomes you measure. If you control X (i.e., time, dose, concentration), don't use correlation, use linear regression. Correlation calculations do not discriminate between X and Y, but rather quantify the relationship between the two variables. Linear regression does discriminate between X and Y. Linear regression finds the best line that predicts Y from X by minimizing the sum of the square of the vertical distances of the points from the regression line. The X and Y variables are not symmetrical in the regression calculations. Therefore only choose regression, rather than correlation, if you can clearly define which variable is X and which is Y. 


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