Rent-A-Car Project
Datasets and description for the case assignments:
1. 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)
2. 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.
3. 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
4. Compute elasticities for PownE_X1, PownL_X2, and pcomp_X3 for week 30.
5. 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?
6. 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