3-Period Moving Average
provides a better historical fit based on the MAD criterion.
Question 4 |
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Demand at Nature Trails Ski Resort has a seasonal pattern. Demand is highest during the winter, as this is the peak ski season. However, there is some ski demand in the spring and even fall months. The summer months can also be busy as visitors often come for summer vacation to go hiking on the mountain trails. The owner of Nature Trails would like to make a forecast for each season of the next year. Total annual demand has been estimated at 4,020 visitors. Given the last two years of historical data, what is the forecast for each season of the next year?
Visitors |
||
Season |
Year 1 |
Year 2 |
Fall |
196 |
225 |
Winter |
1,415 |
1,616 |
Spring |
504 |
601 |
Summer |
693 |
818 |
(Round your answers to 0 decimal place, the tolerance is +/-1.)
Season |
Forecast |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Fall |
b. Using the data, what would you expect sales to be if training was increased to eighteen hours? Use the linear regression model. (Round your answer to 2 decimal places, the tolerance is +/-0.01.)
Small Wonder, an amusement park, experiences seasonal attendance. It has collected two years of quarterly attendance data and made a forecast of annual attendance for the coming year. Compute the seasonal indexes for the four quarters and generate quarterly forecasts for the coming year, assuming annual attendance for the coming year to be 1525.
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