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




Question;Rent-A-Car ProjectDue date: June 15, 2014Datasets 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+ a12X12Compute 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 setVariable TypeVariable NameVariable ExplanationDependent variableQE_YNumber of rental contracts initiated each week in the economy categoryIndependent variablePownE_X1Average daily rate Rent-A-Car charged for its economy cars in a given weekIndependent variablePownL_X2Average daily rate Rent-A-Car charged for its luxury vehicles in a given weekIndependent variablePcomp_X3Average daily rate of the only competitor across all vehicle categoriesIndependent variableSession_X4Binary variable with 1 indicating weeks when college is in sessionIndependent variableWeather_X5Number of days in a week with severe weatherIndependent variableUnemployment_X6Number of unemployed workers in the county as of Tuesday each weekIndependent variableFlghtWk_X7Number of flights (in- and outbound) serving the local airport that weekIndependent variableCancWk_X8Total number of flights cancelled that weekIndependent variableHoliday_X9Binary variable with 1 indicating weeks of national holidays (long weekends)Independent variableWrecks_x10Number of major accidents that weekIndependent variableTotalAd_X11Amount spent on local advertising each weekIndependent variableFleetAge_X12Average age of our fleet measured in weeks


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