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)^{1}X'Y

Effect

Coefficient

Standard Error

Std. Coefficient

Tolerance

t

pvalue

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

Fratio

pvalue

Regression

7.6326021E+006

2

3.8163011E+006

15.9045153

0.0000874

Residual

4.5590651E+006

19

2.3995079E+005



DurbinWatson 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