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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

Business Economics, Economics

  • Category:- Business Economics
  • Reference No.:- M9745132
  • Price:- $10

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