using excel Regression Estimation In the worksheet called ‘GA data’ you will fin

Question

using excel

Regression Estimation
In the worksheet called ‘GA data’ you will find ground antelope demand quantities, prices, and tomato (another chili input) prices. Dr. Von Nostran would like you to estimate a demand curve for ground antelope, also he is curious if tomato is a complement or substitute for ground antelope.

Qd(antelope) = B0 + or – B1*P(antelope) + or - B2*P(tomato)

(We expect B1 to be negative but do not know for sure, also are not sure if B2 will be + or -; these issues will be resolved when you solve for the Bs)

Use solver to find the B0, B1, and B2 that minimize the sum of squared errors.

Verify your findings using the build in ‘Data Analysis’ regression tool.

GA price Tomato price GA Q Demand
$1.48 $1.11 0.87
$1.50 $1.06 0.98
$1.44 $1.03 2.12
$1.41 $1.00 4.14
$1.37 $1.01 0.98
$1.33 $1.02 1.25
$1.38 $1.04 2.06
$1.36 $0.99 4.10
$1.37 $1.00 1.10
$1.36 $0.98 1.34
$1.33 $1.00 2.15
$1.33 $0.98 4.39
$1.37 $1.16 1.18
$1.58 $1.33 1.29
$1.78 $1.67 2.10
$1.97 $1.21 3.84
$1.73 $1.24 1.20
$1.46 $1.09 1.52
$1.33 $1.08 1.96
$1.39 $1.13 3.99
$1.38 $1.12 1.07
$1.33 $1.11 1.39
$1.36 $1.27 1.89
$1.42 $1.20 3.91
$1.40 $1.11 1.15
$1.36 $1.07 1.52
$1.34 $1.06 2.01
$1.35 $0.95 4.22
$1.29 $0.99 1.22
$1.23 $1.01 1.39
$1.26 $1.01 2.11
$1.30 $0.96 4.02
$1.18 $0.98 1.22
$1.23 $1.04 1.59
$1.28 $1.07 2.03
$1.30 $1.00 3.88
$1.31 $1.01 1.35
$1.27 $0.98 1.76
$1.22 $0.89 2.12
$1.16 $0.85 4.02
$1.18 $0.86 1.78
$1.10 $0.78 1.95
$1.12 $0.90 2.58
$1.15 $0.90 3.95
$1.12 $0.86 1.60
$1.10 $0.80 1.90
$1.10 $0.81 2.50
$0.99 $0.75 4.56
$0.98 $0.76 1.73
$0.95 $0.75 2.03
$0.98 $0.74 2.59
$0.95 $0.71 4.21
$0.94 $0.71 2.03
$0.93 $0.69 2.16
$0.92 $0.74 2.47
$0.90 $0.76 4.36
$0.92 $0.83 1.91
$0.94 $0.80 2.16
$0.97 $0.77 2.60
$0.97 $0.72 4.38
$1.01 $0.73 1.99
$0.96 $0.71 2.16
$0.97 $0.70 2.73
$0.96 $0.70 4.70
$0.97 $0.70 2.32
$0.95 $0.71 2.38
$0.99 $0.84 3.01
$0.98 $0.80 5.17
$0.93 $0.73 2.53
$0.91 $0.69 2.88
$0.92 $0.68 3.50
$0.81 $0.66 5.82
$0.80 $0.64 3.04
$0.78 $0.69 3.41
$0.83 $0.80 3.79
$0.83 $0.75 5.45
$0.80 $0.74 3.12
$0.81 $0.77 3.28
$0.83 $0.76 4.19
$0.77 $0.71 6.01
$0.77 $0.70 3.54
$0.76 $0.70 3.68
$0.77 $0.69 4.21
$0.73 $0.65 6.13
$0.74 $0.66 3.66
$0.75 $0.65 3.91
$0.75 $0.64 4.04
$0.69 $0.63 6.28
$0.69 $0.62 3.45
$0.70 $0.61 3.73
$0.71 $0.62 4.20
$0.66 $0.62 6.49
$0.69 $0.61 3.55
$0.70 $0.61 3.73
$0.71 $0.62 3.98
$0.67 $0.62 6.45
$0.67 $0.61 3.55
$0.67 $0.61 3.77
$0.69 $0.61 4.41
$0.67 $0.60 6.10
$0.66 $0.60 3.56
$0.68 $0.59 3.91
$0.70 $0.60 4.16
$0.65 $0.61 6.17
$0.68 $0.61 3.72
$0.66 $0.61 3.90
$0.68 $0.63 4.64
$0.65 $0.64 6.21
$0.66 $0.63 3.46
$0.66 $0.62 3.98
$0.67 $0.62 4.20
$0.62 $0.62 5.96
$0.62 $0.63 3.92
$0.60 $0.63 3.92
$0.63 $0.65 4.17
$0.59 $0.65 6.01
$0.60 $0.65 3.84
$0.58 $0.63 3.83
$0.61 $0.63 4.33

Explanation / Answer

here the model is

GA_Qd=6.219-2.90*GA_price-0.100*Tomato_price

here B1 and B2 are negative

SUMMARY OUTPUT Regression Statistics Multiple R 0.63048 R Square 0.397505 Adjusted R Square 0.387118 Standard Error 1.143352 Observations 119 ANOVA df SS MS F Significance F Regression 2 100.0479 50.02396 38.26643 1.73E-13 Residual 116 151.6415 1.307254 Total 118 251.6894 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 6.219318 0.459047 13.54834 1.19E-25 5.310118 7.128517 5.310118 7.128517 GA_price -2.89729 1.002612 -2.88974 0.004603 -4.88309 -0.91149 -4.88309 -0.91149 Tomato_price -0.09569 1.512735 -0.06325 0.949672 -3.09185 2.900474 -3.09185 2.900474

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