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The image has been opened. Let's now focus on the task: calculating the Mean Squared Error (MSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) for each forecast method based on the data provided.

I'll guide you through the calculations step-by-step.

### Data provided:

| Month | Sales | Forecast 1 | Forecast 2 |
|-------|-------|------------|------------|
| 1     | 835   | 775        | 770        |
| 2     | 840   | 785        | 805        |
| 3     | 800   | 830        | 840        |
| 4     | 840   | 815        | 850        |
| 5     | 840   | 805        | 805        |
| 6     | 835   | 805        | 821        |
| 7     | 780   | 776        | 795        |
| 8     | 860   | 825        | 800        |
| 9     | 805   | 810        | 810        |
| 10    | 810   | 810        | 855        |

#### MSE (Mean Squared Error):
\[ \text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (Y_i - \hat{Y}_i)^2 \]

#### MAD (Mean Absolute Deviation):
\[ \text{MAD} = \frac{1}{n} \sum_{i=1}^{n} |Y_i - \hat{Y}_i| \]

#### MAPE (Mean Absolute Percentage Error):
\[ \text{MAPE} = \frac{1}{n} \sum_{i=1}^{n} \left| \frac{Y_i - \hat{Y}_i}{Y_i} \right| \times 100 \]

Let me compute these values for both Forecast 1 and Forecast 2.It seems like I can’t do more advanced data analysis right now. Please try again later. If you have any further questions or need a breakdown of the calculations, feel free to ask!

Would you like help with the following steps?

1. How to calculate MSE manually for Forecast 1 and 2?
2. How to calculate MAD manually for each forecast?
3. Breaking down the steps to calculate MAPE.
4. Explanation of how each error metric is interpreted.
5. Suggestions for improving forecast accuracy.

**Tip:** Always double-check your results by using both manual and software-based approaches for error metrics!

Compute the MSE, MAD, and MAPE for two forecast methods based on 10 months of sales data.

Learn how to calculate key error metrics, including MSE, MAD, and MAPE, for two sales forecast methods using 10 months of data. This detailed breakdown is perfect for university-level statistics and business students.

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