Which statement correctly describes MAPE and RMSE and their interpretation?

• A. MAPE measures the magnitude of forecast errors in percentage terms, and RMSE measures the average of squared errors, with higher values indicating better accuracy.
• B. MAPE is mean absolute percentage error; RMSE is root mean squared error; lower values indicate better accuracy.
• C. MAPE and RMSE are not used to assess forecast accuracy.
• D. RMSE expresses errors as percentages, while MAPE expresses errors in the units of the forecast.

Answer: (B)

The main idea is understanding what MAPE and RMSE tell us about forecast accuracy and how to read their values. MAPE, or mean absolute percentage error, expresses average forecast error as a percentage, making it easy to compare accuracy across different scales. RMSE, or root mean squared error, measures the typical error in the same units as the forecast, but because errors are squared before averaging, it gives more weight to larger mistakes. In both cases, lower values mean better accuracy.

The statement that correctly describes them and their interpretation is the one that defines both metrics and notes that lower values indicate better accuracy. The other options mix up what each metric measures or how they’re interpreted: higher values do not indicate better accuracy, forecast accuracy is indeed assessed with these metrics, RMSE is not a percentage, and MAPE is not in the forecast’s units.