Question
Example 1. Green Tomato Restaurant Month Sales (000s) January $ 42 February $ 49 March $ 59 April $ 39 May $ 56 June $
Example 1.
| Green Tomato Restaurant | |
| Month | Sales (000s) |
| January | $ 42 |
| February | $ 49 |
| March | $ 59 |
| April | $ 39 |
| May | $ 56 |
| June | $ 59 |
| July | $ 50 |
| August | $ 49 |
| September | $ 59 |
| October | $ 39 |
| November |
|
| December |
|
Using the above information, answer the following questions. Round answers to the nearest $.
1. What is the forecast for November using a 4-month moving average approach?
Answer:
2. What is the forecast for November using a 3-month weighted moving average approach?
In order from most recent to least recent month, assign weights of 3, 2, and 1.
Answer:
3. What is the forecast for November using exponential smoothing with alpha = 0.10.
October forecast was 50.
Answer:
Example 2.
The following is a statistical regression output for MacHall Caf, the only restaurant at the MacHall Hotel.
| 2016 | X | Y | X = guests in hotel Y = covers served |
| January | 4060 | 5200 | |
| February | 4100 | 5360 | |
| March | 4200 | 572 | |
| April | 4250 | 5430 | |
| May | 4200 | 5680 | |
| June | 4150 | 5520 | |
| July | 4300 | 5800 | |
| August | 4350 | 5910 | |
| September | 4400 | 6020 | |
| October | 4200 | 5840 | |
| November | 4080 | 5510 | |
| December | 3600 | 5020 |
| Summary Output | |
| Multiple R | 0.856821 |
| R square | 0.734142 |
| Adjusted R square | 0.707556 |
| Standard error | 161.8479 |
| Observations | 12 |
|
| Coefficients | Std. error | t Stat | p-value | Lower 95% | Upper 95% |
| Intercept | 369.7173 | 993.400 | 0.372 | 0.718 | 1,843.715 | 2,583.149 |
| X variable | 1.254227 | 0.239 | 5.255 | 0.000 | 0.722 | 1.786 |
1. Explain the relationship between the independent and dependent variable.
Answer:
2. Write a regression equation for forecasting the number of covers at the MacHall Caf.
Answer:
3. Assuming 3000 rooms have been pre-sold for January 2017 with an average guest occupancy of 1.5 per room, forecast the number of covers for the MacHall Caf for January 2017.
Answer:
Example 3.
The Evergreen Hotel has 200 rooms and uses regression analysis to forecast dining room meals. The regression equations are as follow:
- Breakfast: y = 50 + 0.42x
- Lunch: y = 200 + 0.21x
- Dinner: y = 450 + 0.35x
- x = number of hotel guests
- y = forecasted meals sold
The average check in the hotel dining room are as follow:
- Breakfast: $3.25
- Lunch: $6.50
- Dinner: $12.95
Forecast daily dining room revenue by meal period when the hotel expects occupancy of 85% with average room occupancy of 1.58 guests per room.
Answer:
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