Question
Unfortunately, your specific data request has been rejected by the franchise owners. However, they have provided a data set that includes a number of useful
Unfortunately, your specific data request has been rejected by the franchise owners. However, they have provided a data set that includes a number of useful variables for the past 52 weeks. The data is in the Excel file and descriptions of the variables are available in your team OneNote as well as below.
Use this data, as best you can, to forecast the demand for "economy" cars in the next four weeks. It would be even better if you could suggest a specific rate that we should charge for the "economy" category to maximize revenue. How many vehicles do you expect to be rented at the rate you are suggesting? Do we need to worry about increasing our fleet if we follow your demand forecast?
In your response, include not only your forecast and response to the questions, but also explain why you have chosen the specific independent variables you have used in your regression model and their expected signs. Be sure to discuss the goodness of fit of your model, and the statistical significance of the regression coefficients.
You should also submit an Excel file (or output from whatever regression software used) showing full results from any analyses.
| Regression Statistics | ||||||||
| Multiple R | 0.95970952 | |||||||
| R Square | 0.92104236 | |||||||
| Adjusted R Square | 0.89403054 | |||||||
| Standard Error | 3.40932766 | |||||||
| Observations | 52 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 13 | 5152.36412 | 396.335701 | 34.0977491 | 6.4494E-17 | |||
| Residual | 38 | 441.693573 | 11.6235151 | |||||
| Total | 51 | 5594.05769 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 24.8013626 | 9.26390344 | 2.67720435 | 0.01090065 | 6.04757051 | 43.5551546 | 6.04757051 | 43.5551546 |
| PownE | -0.5190898 | 0.25998093 | -1.9966455 | 0.05306393 | -1.0453936 | 0.00721412 | -1.0453936 | 0.00721412 |
| PownL | 0.20035475 | 0.12706801 | 1.57675208 | 0.12314228 | -0.056881 | 0.4575905 | -0.056881 | 0.4575905 |
| Pcomp | 0.09038534 | 0.1482169 | 0.60981806 | 0.54561189 | -0.2096641 | 0.39043478 | -0.2096641 | 0.39043478 |
| session | -1.0229758 | 1.28024077 | -0.7990495 | 0.42922801 | -3.6146877 | 1.56873617 | -3.6146877 | 1.56873617 |
| weather | 0.80017554 | 1.19240971 | 0.67105755 | 0.50624267 | -1.6137317 | 3.21408281 | -1.6137317 | 3.21408281 |
| unempl | -0.0027721 | 0.00677991 | -0.4088662 | 0.6849331 | -0.0164973 | 0.01095313 | -0.0164973 | 0.01095313 |
| flightswk | 0.09375872 | 0.12371107 | 0.75788458 | 0.45319474 | -0.1566813 | 0.34419869 | -0.1566813 | 0.34419869 |
| cancwk | 0.07351325 | 0.50207499 | 0.14641885 | 0.88436476 | -0.9428844 | 1.08991093 | -0.9428844 | 1.08991093 |
| holiday | -1.5923715 | 1.82020967 | -0.8748286 | 0.38716313 | -5.2771933 | 2.09245032 | -5.2771933 | 2.09245032 |
| wrecks | 0.0077616 | 0.13510175 | 0.05745002 | 0.95448775 | -0.2657376 | 0.28126079 | -0.2657376 | 0.28126079 |
| Discount | 7.09715659 | 0.89544422 | 7.92584998 | 1.4261E-09 | 5.28442453 | 8.90988865 | 5.28442453 | 8.90988865 |
| Upgrade | 2.29540707 | 0.82841633 | 2.77083756 | 0.00860611 | 0.6183659 | 3.97244825 | 0.6183659 | 3.97244825 |
| TotlAd | -0.0005147 | 0.000421 | -1.2225642 | 0.22902315 | -0.001367 | 0.00033757 | -0.001367 | 0.00033757 |
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International Marketing And Export Management
Authors: Gerald Albaum , Alexander Josiassen , Edwin Duerr
8th Edition
1292016922, 978-1292016924
