compute all possible forecasts using a five-period moving average (round answer to 1 decimal place)

Here are the results for the 5-period moving average forecasts, rounded to one decimal place:

| Period | Demand | 5-period MA |
|--------|--------|-------------|
| 1      | 756    | -           |
| 2      | 894    | -           |
| 3      | 940    | -           |
| 4      | 561    | -           |
| 5      | 516    | -           |
| 6      | 764    | 733.4       |
| 7      | 990    | 735.0       |
| 8      | 855    | 754.2       |
| 9      | 965    | 737.2       |
| 10     | 940    | 818.0       |
| 11     | 718    | 902.8       |
| 12     | 630    | 893.6       |
| 13     | 437    | 821.6       |
| 14     | 558    | 738.0       |
| 15     | 650    | 656.6       |
| 16     | 437    | 598.6       |
| 17     | 558    | 542.4       |

The forecasts for periods 1 through 5 are not available since there aren't enough previous data points to calculate a 5-period moving average.

Would you like more details on how these calculations were performed or need help with another question?

Here are some additional topics we could explore:
1. How effective is the 5-period moving average in predicting demand?
2. Compute moving average forecasts for different periods and compare their effectiveness.
3. Discuss the advantages of using a longer period for moving averages.
4. Analysis of demand stability over the periods using statistical methods.
5. Evaluate the variability in demand with standard deviation.
6. Predict future demand based on moving average trends.
7. Calculate the percentage error of 5-period moving average forecasts.
8. Visualize the demand and moving average forecasts to assess their alignment.

**Tip**: A longer moving average period can help reduce the noise further in volatile data, providing a smoother forecast line at the cost of responsiveness to recent changes in data trends.

Learn how to compute 5-period moving average forecasts for demand analysis with a detailed step-by-step solution. This problem covers time series analysis concepts, including the calculation of moving averages and its application in forecasting demand. Suitable for advanced level analysts and students.

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