Reference no: EM13151097
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.
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Dependent Variable
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CARS
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N
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22
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Multiple R
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0.7912337
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Squared Multiple R
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0.6260507
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Adjusted Squared Multiple R
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0.5866876
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Standard Error of Estimate
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489.8477246
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Regression Coefficients B = (X'X)-1X'Y
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Effect
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Coefficient
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Standard Error
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Std.
Coefficient
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Tolerance
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t
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p-value
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CONSTANT
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3,256.1687058
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913.1082505
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0.0000000
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.
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3.5660270
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0.0020617
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PRICE
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-289.3428493
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64.1230827
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-0.6330709
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0.9998873
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-4.5123041
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0.0002384
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PITTDECT
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82.8148154
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24.8288902
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0.4679557
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0.9998873
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3.3354216
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0.0034764
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Analysis of Variance
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Source
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SS
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df
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Mean Squares
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F-ratio
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p-value
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Regression
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7.6326021E+006
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2
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3.8163011E+006
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15.9045153
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0.0000874
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Residual
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4.5590651E+006
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19
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2.3995079E+005
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Durbin-Watson D Statistic
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0.9936443
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First Order Autocorrelation
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0.4089696
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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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