Reference no: EM13375516

1.    Load the Blue Spruce Light Up Data (latest file, through 2013).

Extract and specify a model that predicts Cars through the gate as a function of Price and Average Daily Temperature.

 

Dependent Variable	CARS
N	22
Multiple R	0.7912337
Squared Multiple R	0.6260507
Adjusted Squared Multiple R	0.5866876
Standard Error of Estimate	489.8477246

 

Regression Coefficients B = (X'X)-1X'Y
Effect	Coefficient	Standard Error	Std. Coefficient	Tolerance	t	p-value
CONSTANT	3,256.1687058	913.1082505	0.0000000	.	3.5660270	0.0020617
PRICE	-289.3428493	64.1230827	-0.6330709	0.9998873	-4.5123041	0.0002384
PITTDECT	82.8148154	24.8288902	0.4679557	0.9998873	3.3354216	0.0034764

 

Analysis of Variance
Source	SS	df	Mean Squares	F-ratio	p-value
Regression	7.6326021E+006	2	3.8163011E+006	15.9045153	0.0000874
Residual	4.5590651E+006	19	2.3995079E+005

 

Durbin-Watson D Statistic	0.9936443
First Order Autocorrelation	0.4089696

CARS=3256.169 - 289.343(PRICE) + 82.815(TEMP)

Using a hypothetical temperature of 35 degrees and a price of $8, predict the number of cars through the gate.

CARS=3256.169 - 289.343(8) + 82.815(35)

          =3256.169 - 2314.744  + 2898.525 = 3839.96

          = 3840 CARS

Now using a hypothetical temperature of 35 degrees and a price of $9, predict the number of cars through the gate.

CARS=3256.169 - 289.343(9) + 82.815(35)

           =3256.169 - 2604.087 + 2898.525 = 3550.607

           =3551 CARS

Now using a hypothetical temperature of 35 degrees and a price of $10, predict the number of cars through the gate.

CARS=3256.169 - 289.343(10) + 82.815(35)

 =3256.169 - 2893.43 + 2898.525 = 3261.264

            =3261 CARS

 Predicted Total Revenue at $8 = 8(3840) = $30,720

Predicted Total Revenue at $9 = 9(3551) = $31,959

Predicted Total Revenue at $10 = 10(3261) = $32,610

Compute the point price elasticity of demand at $8:

At $9:

At $10:

Based on your point elasticity results which is the better price to charge?

Attachment:- Q5 data.xlsx