Question 1. Trampoline park (25 points)

A relatively young trampoline park company, named Wiggles Out, has been studying the marketdemand within a large city and surrounding area for years. It has recently opened a location between the city and a large suburb that has a substantial population of young families. The co-owners of Wiggles Out have contracted a business demand forecast expert to help them with the forecasting so that they can better prepare for the crowd fluctuations. This expert includes variables such as weather, advertising, and school sessions to create her forecasts. View the table below for a comparison between the co-owners forecasts and the expert's forecasts for the next four weeks.

	Co-Owners' Forecast	Expert's Forecast	True Demand
Week 1	370	260	245
Week 2	270	275	260
Week 3	250	330	375
Week 4	390	380	400

1. What is the co-owners' forecast error on average? (5 point)

Ans:

2. What is the expert's forecast error on average? (5 point)
3. Based on the forecast error averages above, what can we determine? (3 point)
4. In order to gain a better understanding of whose forecast is more accurate, the co-owners decide to analyze forecast errors during the four weeks. What is the mean absolute deviation, or MAD, of the co-owners' four-week forecast? (5 points)
5. What is the mean absolute deviation, or MAD, of the expert's four-week forecast? (5 points)
6. Based on the MAD data for both the co-owners and the expert, who is doing a better job with forecasting the number of jumpers per week? (2 point)

Question 2. Trampoline park (15 points)

Tom's Towing LLC operates a fleet of tow trucks that it sends to help drivers in need on the nearby highway. The numbers of calls requesting a tow truck for Monday, Tuesday Wednesday, and Thursday were 27, 18, 21, and 15, respectively.

1. What would be its forecast for Friday, using a nave forecasting approach? (5 point)
2. What would be its forecast for Friday using a four-day moving average approach? (5 point)
3. What would be its forecast for Friday using an exponential smoothing forecasting approach? Use smoothing factor = 0.4 and a forecast for Monday of 18. (5 points)