Page 179 45 pm Problem 5-29 Use the data from this problem to perform the following (note: your Weeks are in all one column 1 to 12, 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 #B, 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, 1, 1 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? Page 180 Problem 5-30 Use the data from this problem to perform the following: (similarly set up the problem as in class, your time periods will all be in one column, and your actual forecast all in one column, then first forecast column, then error column, etc. A) Using a moving average of 6 time periods, forecast the emergency calls for week 25. B) Using a moving average of 9 time periods, forecast the emergency calls for week 25. C) Assuming an initial forecast of 50 calls in week 1, use exponential smoothing to forecast calls for week 25. Use alpha = .4. D) Calculate the MAD for each forecasting technique and determine which is the most accurate? Why?