A real estate agency collects data concerning y = the sales price of a house (in thousands of dollars), and x = the home size (in hundreds of square feet). The data are given in the table below. The MINITAB output from fitting a least squares regression line to the data is on the next page.

a. By using the formulas illustrated in Example 13.2 and the data provided. Verify that (within rounding) b0 = 48.02 and b1 = 5.700, as shown on the MINITAB output,
b. Interpret the meanings of b0 and b1. Does the interpretation of bf) make practical sense?
c. Write the least squares prediction equation.
d. Use the least squares line to obtain a point estimate of the mean sales price of all houses having 2,000 square feet and a point prediction of the sales price of an individual house having 2.0(H) square feet.

Transcribed Image Text:

Real Estate Sales Price Data Sales Price (y) Size (x) 180 RealEst Sales Price (y) 165.9 193.5 127.8 163.5 172.5 Home Home Size (x) 21 24 13 19 25 23 98.1 173.1 136.5 141 20 17 15 Fitted Line Plot SalesPrice = 48.02 + 5.700 HomeSize 200 o 180 a 160 140 120 100 10 12 14 16 18 20 22 24 26 Home Size