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