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.