Suppose demand for a product is determined by its price, consumers' income, and the price of a related good. Use Q for demand, P for price, M for income, and PR for price of related good. The demand function is estimated using regression analysis. The results are reported below:
|
SUMMARY OUTPUT
|
|
Regression Statistics
|
|
Multiple R
|
0.814752135
|
|
R Square
|
0.663821042
|
|
Adjusted R Square
|
0.159552605
|
|
Standard Error
|
530.2842631
|
|
Observations
|
66
|
|
Coefficients
|
Standard Error
|
t Stat
|
P-value
|
|
Intercept
|
125.56
|
15.87
|
|
|
|
P
|
-5.39
|
2.19
|
???
|
|
|
M
|
0.069
|
0.046
|
|
|
|
PR
|
-10.98
|
2.73
|
|
|
1. What is the of this regression?
2. What is the degrees of freedom of this regression?
3. What is the effect of a one-dollar increase in price (P) on demand (Q)?
4. What is the effect of a one-dollar increase in income (M) on demand (Q)?
5. What is the effect of a one-dollar increase in price of related good (PR) on demand (Q)?
6. Calculate the t Stat (or t ratio marked with "???" in the table) for the coefficient on P?
7. Test whether the effect of P on Q is significant at the 5% significance level. Show your work.
8. Using the p-value 0.046 in the table, test if the effect of M on Q is significant at the 5% significance level.
9. Using the values , ,000, and , predict the demand (Q)?
10. Using the value of predicted Q you just calculated for part 9), calculate the estimates of
The price elasticity of demand. Show your work.
The income elasticity of demand. Show your work.
The cross-price elasticity of demand. Show your work.