dolution
dolution.
| Year | Month | Passengers | |
| Year 1 | January | 13,441,718 | |
| Year 1 | February | 11,942,221 | |
| Year 1 | March | 14,670,996 | |
| Year 1 | April | 14,286,844 | |
| Year 1 | May | 14,537,314 | |
| Year 1 | June | 15,906,101 | |
| Year 1 | July | 17,362,586 | |
| Year 1 | August | 16,969,528 | |
| Year 1 | September | 14,010,920 | |
| Year 1 | October | 13,599,030 | |
| Year 1 | November | 12,919,746 | |
| Year 1 | December | 14,289,105 | |
| Year 2 | January | 13,970,077 | |
| Year 2 | February | 12,230,963 | |
| Year 2 | March | 15,447,435 | |
| Year 2 | April | 14,507,038 | |
| Year 2 | May | 15,516,063 | |
| Year 2 | June | 16,487,702 | |
| Year 2 | July | 17,954,910 | |
| Year 2 | August | 17,786,357 | |
| Year 2 | September | 14,408,817 | |
| Year 2 | October | 14,374,254 | |
| Year 2 | November | 13,258,104 | |
| Year 2 | December | 15,182,616 | |
- What forces seem to be at work here in terms of trend and seasonality? Explore the data and comment on the trend and seasonality patterns present in the data. (3 marks)
- Use the “seasonality forecasting technique with no trend†to forecast the international passenger volume for each month of year 3 in this airport. (Use the actual number of passengers as the initial value for exponential smoothing. Use smoothing constant 0.3) (10 marks)
- Create a graph comparing the actual number of passengers versus forecasted volume from part (b) and comment on how they compare. (2 marks)
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