Rent-A-Car: an integrated team-based case study
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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Solution: Rent-A-Car: an integrated team-based case study