A real estate agency collects the data in Table 14.4 concerning
y = sales price of a house (in thousands of dollars)
x1 = home size (in hundreds of square feet)
x2 = rating (an overall "niceness rating" for the house expressed on a scale from l [worst] to l0 [best|, and provided by the real estate agency)
Scatter plots of y versus A, and y versus x2 are as follows:

The agency wishes to develop a regression model that can be used to predict the sales prices of future houses it will list. Figure 14.6 gives the MINITAB output of a regression analysis of the real estate sales price data in Table 14.4 using the model
y = β0 + β1x1 + β3x2 + ε
a. Using the MINITAB output, identify and interpret b1 and b2, the least squares point estimates of β1, and β2.
b. Calculate a point estimate of the mean sales price of all houses having 2,000 square feet and a rating of 8. and a point prediction of the sales price of a single house having 2,(KK) square feet and a rating of 8. Find this point estimate (prediction), which is given at the bottom of the MINITAB output, and verify that it equals (within rounding) your calculated value.

Transcribed Image Text:

0 0 Rating, x2 Size, x1