Which term describes the measure of how well the regression model predicts the dependent variable?

The correct term that describes the measure of how well the regression model predicts the dependent variable is the coefficient of determination. This statistic is commonly denoted as R-squared, and it quantifies the proportion of variance in the dependent variable that can be explained by the independent variable(s) in the regression model.

A higher R-squared value indicates that the model explains a significant portion of the variability in the dependent variable, suggesting better predictive power. Conversely, a lower R-squared suggests that the model does not explain much of the variability, making it less effective as a predictive tool.

The other options, while related to the context of regression analysis, do not specifically measure the predictive capability of the model in the same way. Predictive accuracy is a broader concept that isn't confined to regression alone, while statistical significance pertains more to the validity of relationships between variables rather than predicting outcomes. Model reliability refers to the consistency of the model over time or across different data sets, but it does not represent the specific measure of prediction effectiveness that R-squared does.