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
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