Rent-A-Car Project Datasets and description for the case assignments: In this project, you are required

Rent-A-Car Project

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) + a ₁ X ₁ + a ₂ X ₂ + a ₃ X ₃ + a ₄ X ₄ + a ₅ X ₅ + a ₆ X ₆ + a ₇ X ₇ + a ₈ X ₈ + a ₉ X ₉ + a ₁₀ X ₁₀ + a ₁₁ X ₁₁ + a ₁₂ X ₁₂
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?

R ent-A-Car: Description o f 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