Dependent Variable: Quantity of Economy Cars
Autor: sbchain • May 15, 2016 • Term Paper • 4,015 Words (17 Pages) • 772 Views
Page 1 of 17
Group Project
Stage 1:
- Dependent Variable: Quantity of economy cars. The units of measurement that will be used in this analysis are quantity. For example, 1 economy car, 2 economy cars, etc.
- Independent variables:
- Price (P): This variable will be important because price is a factor when renting a car. Price is also a factor when 60% of customers are college students. We expect to not see a correlation when prices go up, conjointly rental of cars will decrease.
- Season of semester (S): This variable will be important because when a semester is in session there will be more students to rent cars. We will see a negative correlation when students are not in session. Less students mean less rentals.
- Flights per day(FD): This variable is important because depending on the flights per day, this company will need to have the advantage of supplying an increased amount of cars on these days. There will be a positive relationship when flights increase, the number of car rentals will also increase.
- Holidays(H): This variable is important because during holiday’s more people travel and this mean an increased amount of rentals will be necessary. We will see a positive correlation. Whenever there is an increase in holidays, there will be an increased need for car rentals.
- Weather season(WS): The change of weather depicts when customers need more rentals. For example, summer season individuals travel more, and therefore requiring rentals for their travels. We will see that a negative correlation will occur during the winter season. Once winter is in season a decrease in rentals will portray the season.
Stage 2:
SUMMARY OUTPUT | ||||||||
Regression Statistics | ||||||||
Multiple R | 0.64 | |||||||
R Square | 0.41 | |||||||
Adjusted R Square | 0.36 | |||||||
Standard Error | 8.40 | |||||||
Observations | 52.00 | |||||||
ANOVA | ||||||||
| df | SS | MS | F | Significance F | |||
Regression | 4.00 | 2277.51 | 569.38 | 8.07 | 0.00 | |||
Residual | 47.00 | 3316.55 | 70.56 | |||||
Total | 51.00 | 5594.06 |
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| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% |
Intercept | 82.38 | 15.47 | 5.32 | 0.00 | 51.26 | 113.51 | 51.26 | 113.51 |
PownE | -2.00 | 0.43 | -4.67 | 0.00 | -2.87 | -1.14 | -2.87 | -1.14 |
weather | 5.05 | 1.43 | 3.52 | 0.00 | 2.16 | 7.93 | 2.16 | 7.93 |
flights/wk | 0.59 | 0.19 | 3.07 | 0.00 | 0.20 | 0.98 | 0.20 | 0.98 |
Pcomp | 0.57 | 0.34 | 1.68 | 0.10 | -0.11 | 1.24 | -0.11 | 1.24 |
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