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SUMMARY OUTPUT Regression Statistics Multiple R 0.9863 R Square 0.9729 Adjusted R Square 0.9593 Standard Error 6.9872 Observations 10 ANOVA df SS MS F Significance
SUMMARY OUTPUT | ||||||
Regression Statistics | ||||||
Multiple R | 0.9863 | |||||
R Square | 0.9729 | |||||
Adjusted R Square | 0.9593 | |||||
Standard Error | 6.9872 | |||||
Observations | 10 | |||||
ANOVA | ||||||
df | SS | MS | F | Significance F | ||
Regression | 3 | 10497.07165 | 3499.024 | 71.66989 | 4.33211E-05 | |
Residual | 6 | 292.9283486 | 48.82139 | |||
Total | 9 | 10790 | ||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
Intercept | 205.86 | 19.35 | 10.64 | 0.00 | 158.50 | 253.22 |
PRICE | -12.24 | 1.41 | -8.70 | 0.00 | -15.68 | -8.80 |
INCOME | 1.41 | 0.42 | 3.35 | 0.02 | 0.38 | 2.45 |
ADVERTISING | -3.34 | 1.80 | -1.86 | 0.11 | -7.74 | 1.06 |
Estimated regression equation:
Quantity = 205.86 - 12.24 x Price + 1.41 x Income - 3.34 x Advertising
R2 = 0.9729 implies 97.29 percent of the variation in the dependent variable, indicating a very high fitness of the model
Non-linear relationship.
- Select and estimate any form of non-linear relationship.
- Is the estimated demand function "good"? Why or why not? Compare with the linear form above. Elaborate.
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