Real estate valuation. Refer to Exercise 12.18 (p. 728) for the data set of real estate valuations and a model relating the mean house price per unit area E1y2 of properties to x1 = house age (in years), x2 = distance to the nearest MRT (Mass Rapid Transit, Singapore) station (in meters), and x3 = number of convenience stores around. Now consider a model for E1y2 as a function of both the distance to the nearest MRT station and occupancy status (owner, co-owner, and tenant).

a. Set up the appropriate dummy variables for occupancy status.

b. Write the equation of a model that proposes parallel straight-line relationships between mean house price per unit area E1y2 and distance to the nearest MRT station, one line for each occupancy status.

c. Write the equation of a model that proposes nonparallel straight-line relationships between mean house price per unit area E1y2 and distance to the nearest MRT station, one line for each occupancy status.

d. Fit the model, part

b, to the data saved in the file. Give the least squares prediction equation.

e. Refer to part

d. Interpret the value of the least squares estimate of the beta coefficient multiplied by distance to the nearest MRT station.

f. Fit the model, part

c, to the data saved in the file. Give the least squares prediction equation. g. Refer to part

f. Find the estimated slope of the line relating house price per unit area (y) to distance to the nearest MRT station for owner occupying.