1. GETTING STARTED Case 3 A large car fleet company asked us to help them fore- cast vehicle re-sale values. They purchase new vehicles, lease them out for three years, and then sell them. Better forecasts of vehicle sales values would mean better control of profits; understanding what affects resale values may allow leasing and sales policies to be developed in order to maximize profits. At the time, the resale values were being forecast by a group of specialists. Unfortunately, they saw any statistical model as a threat to their jobs, and were uncooperative in providing information. Nevertheless, the company provided a large amount of data on previous vehicles and their eventual resale values. Case 4 In this project, we needed to develop a model for fore- casting weekly air passenger traffic on major domestic routes for one of Australia's leading airlines. The company required forecasts of passenger numbers for each major domestic route and for each class of passenger (economy class, business class and first class). The company provided weekly traffic data from the previous six years. Air passenger numbers are affected by school holidays, major sporting events, advertising campaigns, competition behaviour, etc. School holidays often do not coincide in different Aus- tralian cities, and sporting events sometimes move from one city to another. During the period of the historical data, there was a major pilots' strike during which there was no traffic for several months. A new cut-price airline also launched and folded. Towards the end of the historical data, the airline had trialled a redistribution of some economy class seats to business class, and some business class seats to first class. After several months, however, the seat classifications reverted to the original distribution. 1.6 The basic steps in a forecasting task A l le five bacie stene bmit homework online via Canvas. For full credit, show ALL work (all R code, outputs, etc.). 1. Review Case 3 and Case 4 in Section 1.5 of Forecasting: Principles and Practice. Assuming that the relevant data are available, list four possible predictor variables that may be useful to each case. bmit homework online via Canvas. For full credit, show ALL work (all R code, outputs, etc.). 1. Review Case 3 and Case 4 in Section 1.5 of Forecasting: Principles and Practice. Assuming that the relevant data are available, list four possible predictor variables that may be useful to each case. 1. GETTING STARTED Case 3 A large car fleet company asked us to help them fore- cast vehicle re-sale values. They purchase new vehicles, lease them out for three years, and then sell them. Better forecasts of vehicle sales values would mean better control of profits; understanding what affects resale values may allow leasing and sales policies to be developed in order to maximize profits. At the time, the resale values were being forecast by a group of specialists. Unfortunately, they saw any statistical model as a threat to their jobs, and were uncooperative in providing information. Nevertheless, the company provided a large amount of data on previous vehicles and their eventual resale values. Case 4 In this project, we needed to develop a model for fore- casting weekly air passenger traffic on major domestic routes for one of Australia's leading airlines. The company required forecasts of passenger numbers for each major domestic route and for each class of passenger (economy class, business class and first class). The company provided weekly traffic data from the previous six years. Air passenger numbers are affected by school holidays, major sporting events, advertising campaigns, competition behaviour, etc. School holidays often do not coincide in different Aus- tralian cities, and sporting events sometimes move from one city to another. During the period of the historical data, there was a major pilots' strike during which there was no traffic for several months. A new cut-price airline also launched and folded. Towards the end of the historical data, the airline had trialled a redistribution of some economy class seats to business class, and some business class seats to first class. After several months, however, the seat classifications reverted to the original distribution. 1.6 The basic steps in a forecasting task f i l larile five basic stene 1. GETTING STARTED Case 3 A large car fleet company asked us to help them fore- cast vehicle re-sale values. They purchase new vehicles, lease them out for three years, and then sell them. Better forecasts of vehicle sales values would mean better control of profits; understanding what affects resale values may allow leasing and sales policies to be developed in order to maximize profits. At the time, the resale values were being forecast by a group of specialists. Unfortunately, they saw any statistical model as a threat to their jobs, and were uncooperative in providing information. Nevertheless, the company provided a large amount of data on previous vehicles and their eventual resale values. Case 4 In this project, we needed to develop a model for fore- casting weekly air passenger traffic on major domestic routes for one of Australia's leading airlines. The company required forecasts of passenger numbers for each major domestic route and for each class of passenger (economy class, business class and first class). The company provided weekly traffic data from the previous six years. Air passenger numbers are affected by school holidays, major sporting events, advertising campaigns, competition behaviour, etc. School holidays often do not coincide in different Aus- tralian cities, and sporting events sometimes move from one city to another. During the period of the historical data, there was a major pilots' strike during which there was no traffic for several months. A new cut-price airline also launched and folded. Towards the end of the historical data, the airline had trialled a redistribution of some economy class seats to business class, and some business class seats to first class. After several months, however, the seat classifications reverted to the original distribution. 1.6 The basic steps in a forecasting task A l le five bacie stene bmit homework online via Canvas. For full credit, show ALL work (all R code, outputs, etc.). 1. Review Case 3 and Case 4 in Section 1.5 of Forecasting: Principles and Practice. Assuming that the relevant data are available, list four possible predictor variables that may be useful to each case. bmit homework online via Canvas. For full credit, show ALL work (all R code, outputs, etc.). 1. Review Case 3 and Case 4 in Section 1.5 of Forecasting: Principles and Practice. Assuming that the relevant data are available, list four possible predictor variables that may be useful to each case. 1. GETTING STARTED Case 3 A large car fleet company asked us to help them fore- cast vehicle re-sale values. They purchase new vehicles, lease them out for three years, and then sell them. Better forecasts of vehicle sales values would mean better control of profits; understanding what affects resale values may allow leasing and sales policies to be developed in order to maximize profits. At the time, the resale values were being forecast by a group of specialists. Unfortunately, they saw any statistical model as a threat to their jobs, and were uncooperative in providing information. Nevertheless, the company provided a large amount of data on previous vehicles and their eventual resale values. Case 4 In this project, we needed to develop a model for fore- casting weekly air passenger traffic on major domestic routes for one of Australia's leading airlines. The company required forecasts of passenger numbers for each major domestic route and for each class of passenger (economy class, business class and first class). The company provided weekly traffic data from the previous six years. Air passenger numbers are affected by school holidays, major sporting events, advertising campaigns, competition behaviour, etc. School holidays often do not coincide in different Aus- tralian cities, and sporting events sometimes move from one city to another. During the period of the historical data, there was a major pilots' strike during which there was no traffic for several months. A new cut-price airline also launched and folded. Towards the end of the historical data, the airline had trialled a redistribution of some economy class seats to business class, and some business class seats to first class. After several months, however, the seat classifications reverted to the original distribution. 1.6 The basic steps in a forecasting task f i l larile five basic stene
