Rent-A-Car Project

Rent-A-Car Project

Datasets and description for the case assignments:

http://www.washburn.edu/sobu/dnizovtsev/RentacarCase.html

1. Estimate the demand foreconomyvehicles using variables provided, you might also derive data from other resources and combine with the dataset. Choose the best model.

2. Forecast the demand for theeconomyvehicles in Week 30.

3. Our customers who choose to keep a car for an extra day are currently paying the same base daily rate. Do you see any potential in exploring alternative schemes? If so, what changes should we implement-shall we change the price for extra days? What considerations are involved in this decision? Provide your thoughts on this issue.

4. What other issues or news you would pay attention to adjust your price and managerial strategies?

Demand estimation model selection guidelines

1. Find a model with adjusted R-squared above 0.7

2. Coefficient for PownE should be negative and significant (|t|>2)

3. At least half of the coefficients should bear the correct signs (you should be able to explain the signs) and be statistically significant (|t|>2)

Estimating and Interpreting the Regression
Coefficients Among the software packages used by economists to conduct a regression analysis
of the demand for a good or service are SPSS, 8A8, and Ft (an open-source
software). To estimate the demand for pizza, we employed the regression function
contained in Excel. Although it only contains the basic elements of regression (e.g., it
does not provide a Durbin-Watson statistic), we believe it is perfectly suitable for
many types of regression analysis that would be conducted in business research.
Besides, Excel is more available in both businesses and colleges and universities
than are statistical software packages. Using Excel to perform a regression analysis of the data in Table 5.3 , we obtain
the following output (see Excel Sheet 5.1 ). Based on this output, we can express the following regression equation. 05m? = 59%;? - 008$stij + Eldoosésngthn - 9dg183%P50f,d,ink - [(leSLS'gUrban - ?62§3?Res.tdental R2 = 0.885 82 = 0.860 F = 36.8 Standard Error of Estimate, SEE=1.06
The standard error of each coefficient, SEC, is listed beneath each coefficient in parentheses.