In MLR, the square of the multiple correlation coefficient or R2 is called the*1 pointCross-productVarianceBig RCoefficient of determinationCovariance

In Multiple Linear Regression (MLR), the square of the multiple correlation coefficient, denoted as $R^2$, is referred to as the Coefficient of Determination.

Explanation:

1. Coefficient of Determination: The $R^2$ value represents the proportion of variance in the dependent variable that can be explained by the independent variables in the regression model. It provides insight into the goodness-of-fit of the model.
2. Interpretation: An $R^2$ value of 1 indicates that the regression model perfectly explains all the variance in the dependent variable, while an $R^2$ value of 0 indicates that the model explains none of the variance.

Thus, the correct answer to your question is Coefficient of Determination.