Correlation analysis gives us the correlation coefficient which is a measure of the strength and the direction of the linear association between the variables. This information can be used to decide the suitability of model calibration using a linear regression analysis. The square of the correlation coefficient may be thought of as the percentage of the total variation in y that is explained by the association of y and x. Hence, for R = 1, all the variation is explained by the linear association between the two variables. In this case, all the observations will lie on a straight line of slope /, passing through the point (,).
Another measure used to evaluate the goodness of fit is the standard deviation of the errors , defined as
= (-y_{i})^{2}, | (9) |