To calculate the Mean Absolute Deviation (MAD) for a forecast, we use the formula:

$\text{MAD} = \frac{\sum | \text{Actual} - \text{Forecast} |}{n}$

where:

• $n$ is the number of periods (in this case, five months).
• $| \text{Actual} - \text{Forecast} |$ represents the absolute error for each month.

To solve this accurately, I would need the actual and forecasted values for each of the five months to compute the absolute deviations and their mean. If you could provide these values, I can walk through each step of the calculation and confirm which option (B, C, D, or E) is correct.

Would you like to share the data for further calculation?

---

Here are five related questions that explore this topic further:

1. What is the significance of the mean absolute deviation in forecasting?
2. How does MAD compare to other error metrics like mean squared error?
3. In what scenarios might a zero or negative MAD be relevant?
4. How does MAD help in improving forecasting models?
5. What are the implications of a high MAD on decision-making?

Tip: MAD is valuable for understanding forecast accuracy because it avoids complications from squared values, providing a direct view of average errors.