Final answer:
Forecast error is calculated as the actual demand minus the forecasted demand for a given period. This calculation contributes to evaluating the accuracy and precision of predictions, serving as a foundation for measures like RSFE, MAPE, and MFE, and is fundamental in fields like economics for stock market predictions.
Step-by-step explanation:
The student's question is centered around the calculation of forecast error. The correct formula for forecast error is the actual demand for period t minus the forecasted demand for period t. This calculation is critical for assessing the accuracy and precision of forecasts in various fields, such as economics or supply chain management. Forecast accuracy includes measures like the running sum of forecast errors (RSFE), the mean absolute percentage error (MAPE), and the mean forecast error (MFE).
Accurate forecasts translate to predictions that are close to the actual outcomes, signifying a systematic approach with minimal error. Precision, on the other hand, refers to how close repeated measurements or forecasts are to each other, which is affected by random errors. Confidence intervals provide a range within which the true value is expected to lie, factoring in a margin of error. This concept is crucial in predicting outcomes, such as an economist may do when forecasting stock market indexes.