Page 179_ Problem 5-29 Use the data from this problem to perform the following (note: your Weeks are in all one column 1 to 124 then second column is Passenger Miles for all 12 time periods) ie make Column 1 the week, Column 2 is the miles... you will create Column 3 forecast for #A, Column 4 Error for #A (so can figure MAD), Column 5 forecast for #B, Column 6 Error for #B8, Column 7 forecast for #C, Column 8 Error for #C etc. Saves you from having to repeat the provided data multiple times (unless you wish to separate them) A) Using a weighted moving average with 5 time periods, forecast the miles for week 13. Use a weight of 3 for the most recent, a weight of 3 for the next most recent, a weight of 2 for the next most, a weight of 1 for the next most, and a weight of 1 for the next ie 3, 3, 2, 1l respectively. B) Assuming an initial forecast for week 1 of 17,000 miles, use exponential smoothing to forecast miles for week 13. Use alpha = .2. C) Assuming an initial forecast for week 1 of 17,000 miles, use exponential smoothing to forecast miles for week 13. Use alpha = .6. D) Calculate the MAD for each forecasting technique and determine which is the most accurate? Why? - el 2 t 5-29 Passenger miles flown on Northeast Airlines, a com- muter firm serving the Boston hub, are as follows for the past 12 weeks: ACTUAL ACTUAL PASSENGER PASSENGER MILES MILES (1,000s) WEEK (1,000s) 7 [T (a) Assuming an initial forecast for week 1 of 17,000 miles, use exponential smoothing to compute miles for weeks 2 through 12. Use a = 0.2. (b) What is the MAD for this model? (c) Compute the RSFE and tracking signals. Are they within acceptable limits