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Jihye Chun Kyungjin Lee Rick Jantz Yang Song
Bruin at Work Jihye Chun Kyungjin Lee Rick Jantz Yang Song
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Regression Statistics
Cost Regression SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 5 ANOVA df SS MS F Significance F Regression 2 3.23E-05 Residual 2000 1000 Total 4 Coefficients t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 7680 X Variable 1 15.94 X Variable 2
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Regression Statistics
Revenue Regession SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 5 ANOVA df SS MS F Significance F Regression 2 3.05E+08 1.52E+08 Residual 585636 Total 4 3.06E+08 Coefficients t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept X Variable 1 X Variable 2
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Basic Equations TC = 7680 + 15.94Q – 0.0012Q2
TR = Q – Q2 Profit = Q Q2
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Optimal Quantity and Price
d(Profit)/d(Q) = Q – 3.31 Q = 2364 P(Q) = TR/Q = 21203/Q – Q P(2364) =
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A General Model Two regressions: Cost and Revenue
Two X-values used in regression: Q and Q2 Inputs can be altered to fine the optimal price Green = input Red = output B1 B2 B3 Cost 7680 15.94 Revenue 21203 12.63 Profit 13523 -3.31 0.0007 Optimal Values Q P 20.42
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Regression Statistics
Increase in Demand B1 B2 B3 Cost 7680 15.94 Revenue 23323 12.63 Profit 15643 -3.32 0.0008 Optimal Values Q P 22.18 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 5 ANOVA df SS MS F Significance F Regression 2 3.69E+08 1.84E+08 Residual Total 4 3.7E+08 Coefficients t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept X Variable 1 X Variable 2
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Optimistic Estimations
B1 B2 B3 Cost 7680 15.94 Revenue 19082 12.63 Profit 11402 -3.31 0.0007 Optimal Values Q P 18.71 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 5 ANOVA df SS MS F Significance F Regression 2 2.47E+08 1.23E+08 Residual Total 4 2.48E+08 Coefficients t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept X Variable 1 X Variable 2
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Conclusions Optimal Price is slightly higher than current price
Our Analysis is very sensitive to changing circumstances Given the data available, P = 20 is a reasonable Price As demand increases, we should raise our price and cut our quantity produced
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