Suppose you want to determine how housing prices are set in your area. After gathering data on 220 houses, you estimate the following model"

Pi = 118 + 0.45Bdr+23.4Bath+0.136Hsize+0.008Lsize-0.091Age- 51.3Poor

P represents the price of the house in thousands, Bdr is the number of bedrooms, Bath is the number of bathrooms, Hsize gives the total square feet of the house, Lsize gives the total square feet of the lot, Age represents the number of years since the house was built, and poor is a variable equal to one if the house is in poor condition and 0 otherwise.

A). Find at least two independent variables that you expect to be correlated with each other and describe why this might be the case.

B.) Given the correlation you described above, how would the parameter estimates change if you did not include one of the variables you listed above in your estimation.

C.) Suppose that a homeowner converts part of an existing family room into a new bathroom. What is the regression's prediction of the change in value of the house?

D.) Suppose that a homeowner adds a new bedroom which increases the total size of the house by 400 square feet. What is the regression's prediction of the change in the value of the house?

E.) What is your prediction of the change in value of the house if the homeowner allows the condition to deteriorate to the point where it would be considered in poor condition?

F.) Name at least one other variable that would affect the price of a home.

G.) Assume the standard error on the coefficient on Hsize is 0.07. Test whether the coefficient is significantly larger than 0 at the 99% confidence level. Make sure you complete all five steps of a hypothesis test.

H.) Assume the standard error on the coefficient on Age is 0.04. Test whether the coefficient is significantly less than 0 at the 95% confidence level. Make sure you complete all five steps of a hypothesis test.