Final answer:
The coefficient of determination, represented as R², measures how well the forecast predicts actual values by showing the proportional variance explained by the independent variable in the regression model. It is calculated by squaring the correlation coefficient, r.
Step-by-step explanation:
The measure that indicates how well the forecast is predicting the actual values among the given options is the coefficient of determination, often represented by R². The coefficient of determination represents the proportion of variance in the dependent variable that can be predicted from the independent variable, thus showing the degree to which the variance in the dependent variable can be explained by the independent variable. In simpler terms, it measures the goodness of fit of the regression model to the actual data. The coefficient of determination is calculated from the correlation coefficient, r, which measures the strength and direction of the linear relationship between two variables. By squaring the correlation coefficient (r²), we obtain the coefficient of determination. A value closer to 1 indicates a good fit, meaning that a large proportion of the variance in the outcome variable is explained by the predictor(s) in the model. In the context of the example provided, an economist would use the coefficient of determination to see how well his model predicts outcomes on the stock market by comparing the expected points to the actual points.