Leases This data table includes the annual prices of 223 commercial leases. All of these leases provide office space in a Midwestern city in the United States. In previous exercises, we estimated the...

Leases This data table includes the annual prices of 223 commercial leases. All of these leases provide office space in a Midwestern city in the United States. In previous exercises, we estimated the variable costs (costs that increase with the size of the lease) and fixed costs (those present regardless of the size of the property) using a regression of the cost per square foot on the reciprocal of the number of square feet. The intercept estimates the variable costs and the slope estimates the marginal cost. Some of these leases cover space in the downtown area, whereas others are located in the suburbs. The variable Location identifies these two categories.

(a) Create a scatterplot of the cost per square foot of the lease on the reciprocal of the square feet of the lease. Do you see a difference in the relationship between cost per square foot and 1/Sq Ft for the two locations? Use color-coding or different symbols to distinguish the data for the two locations.

(b) Based on your visual impression formed in part (a), fit an appropriate regression model that describes the fixed and variable costs for these leases. Use a dummy marginal coded as 1 for leases in the city and 0 for suburban leases.

(c) Does the estimated multiple regression fit in part (b) meet the conditions for the MRM?

(d) Interpret the estimated coefficients from the equation fit in part (b), if it is OK to do so. If not, indicate why not.

(e) Would it be appropriate to use the estimated standard errors shown in the output of your regression estimated in part (b) to set confidence intervals for the estimated intercept and slopes? Explain.