The Root Mean Square Error (RMSE) promises to account for the degree of prediction error in model fitness judging from the SD of the residuals. In relation to the use of R square to account for the accurateness of prediction coefficient of a model; is it proper to utilize RMSE in the interpretation of prediction errors unaccounted by the adjustment of R square?

Is there other interpretation in helps to achieve in predictor-criterion context?

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