What statistical measure describes the proportion of variance in the dependent variable that can be explained by the independent variable(s)?

The coefficient of determination is a statistical measure that represents the proportion of variance in the dependent variable that can be explained by the independent variable(s) in a regression analysis. It is often denoted as R². A higher R² value indicates that a greater proportion of the variance in the dependent variable can be accounted for by the model, suggesting a better fit. For instance, an R² value of 0.70 would imply that 70% of the variance in the dependent variable is explained by the independent variables included in the model, which provides valuable insight into the strength and utility of the explanatory model.

In contrast, standard deviation measures the amount of variation or dispersion in a set of values but does not relate to explaining variance between dependent and independent variables. The mean value represents the average of a dataset and doesn't provide information about the relationship between variables. Variance quantifies how much the data points in a dataset differ from the mean but lacks context regarding the relationship between dependent and independent variables in a predictive model.