01 Jun Question Rent-A-Car Project Due date: June 15, 2014
Question
Rent-A-Car Project
Due date: June 15, 2014
Datasets and description for the case assignments:
- In this project, you are required estimate the demand for “economy” vehicles using the variables provided. The dependent variable is QE_Y and there are 11 independent variables (X1 to X11)
- Identify the relationship between the dependent variable (Y) and each of the independent variables (X). For example, the relationship between variable QE_Y and variable PownL_X2 is positive. Economy vehicles and Luxury vehicles are substitute. If the rate of luxury vehicles (and PownL_X2) rises, the quantity demanded for economy vehicles (QE_Y) increases.
- Using Excel or any other statistical software to run regression analysis and estimate the coefficients of each independent variable X. Your model should look like the following:
QE_Y = constant (or intercept) + a1X1+ a2X2+ a3X3+ a4X4+ a5X5+ a6X6+ a7X7+ a8X8+ a9X9+ a10X10+ a11X11+ a12X12
- Compute elasticities for PownE_X1, PownL_X2, and pcomp_X3 for week 30.
- What other factors besides price might be included in this equation? Do you foresee any difficulty in obtaining these additional data or incorporating them in the regression analysis?
- What proportion of the variation in the dependent variable is explained by the independent variables in the equations?
Rent-A-Car: Description of the variables in the data set
| Variable Type | Variable Name | Variable Explanation |
| Dependent variable | QE_Y | Number of rental contracts initiated each week in the economy category |
| Independent variable | PownE_X1 | Average daily rate Rent-A-Car charged for its economy cars in a given week |
| Independent variable | PownL_X2 | Average daily rate Rent-A-Car charged for its luxury vehicles in a given week |
| Independent variable | Pcomp_X3 | Average daily rate of the only competitor across all vehicle categories |
| Independent variable | Session_X4 | Binary variable with 1 indicating weeks when college is in session |
| Independent variable | Weather_X5 | Number of days in a week with severe weather |
| Independent variable | Unemployment_X6 | Number of unemployed workers in the county as of Tuesday each week |
| Independent variable | FlghtWk_X7 | Number of flights (in- and outbound) serving the local airport that week |
| Independent variable | CancWk_X8 | Total number of flights cancelled that week |
| Independent variable | Holiday_X9 | Binary variable with 1 indicating weeks of national holidays (long weekends) |
| Independent variable | Wrecks_x10 | Number of major accidents that week |
| Independent variable | TotalAd_X11 | Amount spent on local advertising each week |
| Independent variable | FleetAge_X12 | Average age of our fleet measured in weeks |
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