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I think you inversed the relationship. Multiple R is the correlation between actual and predicted values of the dependant variable. R2 is the modelâs accuracy in explaining the dependant variable. I think there is a testable difference.
The previous two examples have suggested how we should define the measure formally. In short, the coefficient of determination or r-squared value, denoted r2, is the regression sum of squares divided by the total sum of squares. 1  Since r2 is a proportion, it is always a number between 0 and 1.  If r2 = 1, all of the data points fall perfectly on the regression line.