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