Question: 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.

  1. Select and estimate any form of non-linear relationship.
  2. Is the estimated demand function "good"? Why or why not? Compare with the linear form above. Elaborate.

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