### 1nbspnbspnbsp load the blue spruce light up data latest

##### 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 0.993644 First Order Autocorrelation 0.40897

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

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