What does a higher R-squared value indicate?

• A. Better fit of forecast values to actual values
• B. Higher variance in predicted results
• C. Larger number of data points
• D. Lower mean absolute error

Answer: (A)

A higher R-squared value is indicative of a better fit of forecast values to actual values. R-squared, or the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. When R-squared is closer to 1, it suggests that a greater percentage of variability in the dependent variable can be explained by the model's independent variables, indicating that the model closely aligns with the observed data.

In contrast, a lower R-squared value would suggest that the model does not effectively capture the trend or patterns in the data, leading to poorer predictive accuracy. Therefore, a higher R-squared is desired when assessing the quality of a regression model, as it signals a stronger relationship between the variables and a more reliable forecast.