1. 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:

1) What is the of this regression? R² is how statistically close by measure the data is to the regression line.

2) What is the degrees of freedom of this regression?

66-2 = 64 degrees at .05 = 1.669

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?t= 7.1007992

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 P=100, M=35,000, and PR=40, 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.

2. Suppose the following is an estimated log-linear demand function:

ln Q = 8.99 - 3.78 ln P - 1.77 ln M - 2.03 ln PR

All parameter estimates are significant.

1) Is this good a normal or an inferior good?

2) Is this good a complement of or substitute for the related good? 

3) What is the price elasticity of demand for this good?

4) What is the income elasticity of demand for this good?