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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.
Multiple correlation coefficient, R, is a measure of the strength of the linear relationship between y and the set of variables x 1, x 2, â¦ x p. It is the highest possible simple correlation between y and any linear combination of x 1, x 2 ,â¦., x p. This property explains that the computed value of R is never negative.