Chapter 1 Assignment (Predictor variables) Nirasha Bataduvaarachchi Predictor variables: also known as independent variables are the inputs that are used to determine the changes in outcomes of the dependent variable. Predictor variables can be categorized as, continuous variables, categorial variables, binary variables, ordinal variables and nominal variables. [1] [1] “Predictor variable,” GeeksforGeeks, Jul. 30, 2024. https://www.geeksforgeeks.org/predictor-variable/ Case 3: Forecasting vehicle resale values Predictor variable Explanation Variable type Mileage Higher milage can result in lower resale price. In this case, since the brand-new vehicles are leased for 3 years, the total mileage accumulated during this period must be considered. Continuous variable Age of the vehicle In this example, as new vehicles are leased for three years before resale, their age at the time of resale can be considered as three years for simplicity. However, age can also be considered as a variable based on the time from manufacturing to resale. As vehicle values depreciate over time, older vehicles tend to have lower resale prices. Additionally, depreciation rates can vary depending on market demand. Continuous variable Model year This could capture the generational change of the vehicle by technology, features etc. Categorial variable Engine type/fuel type Depending on the market conditions, different engine types such as petrol, diesel, electric or hybrid could be valued differently. Categorial variable Purchase prices of the vehicle Although depreciation occurs, higher-priced vehicles tend to retain a greater resale price. Continuous variable Brand and model The value of a vehicle is significantly influenced by its brand and model, as certain models are in higher demand in the used car market due to their popularity. Categorial variable Manufacturer For the same brand and model there could be different manufacturers. Depending on the reputation of the manufacturer, reselling prices could change. Nominal variable Accident history A binary variable (yes/no) or if the accident history is to be quantified, the number of accidents and damage cost which determine the severity can be used. Vehicles with higher accident history sell for less value. Binary variable (yes/no) Vehicle condition This could be an ordinal variable where the condition can be categorized as excellent, good, poor etc.. Ordinal variable Number of users during lease Higher number of users usually reduce the resale price Continuous variable Maintenance record The maintenance record reflects how the vehicle has been taken care for over time. A vehicle with a full-service history has a Binary variable (yes/no)
higher resale value than a vehicle with a partial or no service history Transmission This indicates the gear transmission type of the car (Automatic/Manual) which also depends on the market condition Categorial variable Seller type This could be a variable in general depending on whether the seller is a dealership, an independent used car dealer, online marketplace etc. But in this case, since the forecasting is conducted for the specified car company, the seller is not a variable. _ Maximum power The resale price can be influenced by whether the vehicle is a sports or luxury model. Vehicles with high horsepower typically demand a higher resale price due to their performance. Continuous variable References: [2] S. Lessmann and S. Voß, “Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy,” International Journal of Forecasting, vol. 33, no. 4, pp. 864–877, Jul. 2017, doi: 10.1016/j.ijforecast.2017.04.003. [3] L. Bukvić, J. P. Škrinjar, T. Fratrović, and B. Abramović, “Price prediction and classification of UsedVehicles using supervised Machine Learning,” Sustainability, vol. 14, no. 24, p. 17034, Dec. 2022, doi: 10.3390/su142417034. [4] S. Gupta, “Car resale Value Prediction using Machine Learning.” https://www.enjoyalgorithms.com/blog/car-resale-value-predictor-using-random-forest-regressor [5] S. Kumar and A. Sinha, “Predicting Used Car Prices with Regression Techniques,” International Journal of Computer Trends and Technology, vol. 72, no. 6, pp. 132–141, Jun. 2024, doi: 10.14445/22312803/ijctt-v72i6p118. [6] “Why CPO prices are higher — and worth it | Auto Remarketing,” Auto Remarketing, Sep. 23, 2024. https://www.autoremarketing.com/ar/retail/why-cpo-prices-higher-worth-it/ Case 4: Forecasting weekly air passenger traffic on domestic routes A number of predictor variables are given in the example case itself. Predictor variable Explanation Variable type Number of passengers for each class (economy, business, first class) A direct reflection of air passenger demand for the airline Continuous variable School holidays Indicate the travel demand for family vacations which also depends on region and timing Binary variable (yes/no)
Major sporting events Whenever there is a major sporting event, the air traffic from and to hosting cities can increase. Binary variable (yes/no) Advertising campaigns Especially through offers and promotions, advertising can suddenly increase passenger demand. Binary variable (yes/no) Competition behaviors New competitors entering the market can introduce new routes, promotions, and other incentives that attract more customers. This can be represented as a quantifiable variable, influenced by several factors such as the number of new competitors and their market impact. _ Pilots’ strike According to case 4, during the strike there was no air traffic for months. Binary variable (yes/no) Departure time (year/month/day) This could include seasonal, daily, or even hourly patterns. Some passengers may prefer to travel on weekends rather than weekdays, and depending on the distance, the choice to fly during the day or overnight can also influence demand. _ Weather patterns Especially the seasonal weather patterns and extreme weather events can have a huge influence on passenger traffic which can be denoted as temperature. Continuous variable Median per capita income of the region considered References [7], [8] and [9] specifically state the importance of incorporating economic indicators. Higher income levels are linked to increased spending, which leads to more people opting to travel by flight. Continuous variable Population of the region considered The potential pool of passengers is influenced by the amount of population. Continuous variable References: [7] K. A. Lundaeva, Z. A. Saranin, K. N. Pospelov, and A. M. Gintciak, “Demand Forecasting model for airline flights based on historical passenger flow data,” Applied Sciences, vol. 14, no. 23, p. 11413, Dec. 2024, doi: 10.3390/app142311413. [8] T. D. Tolcha, S. Bråthen, and J. Holmgren, “Air transport demand and economic development in subSaharan Africa: Direction of causality,” Journal of Transport Geography, vol. 86, p. 102771, Jun. 2020, doi: 10.1016/j.jtrangeo.2020.102771. [9] B. Chen, X. Zhao, and J. Wu, “Evaluating prediction models for airport passenger throughput using a hybrid method,” Applied Sciences, vol. 13, no. 4, p. 2384, Feb. 2023, doi: 10.3390/app13042384.