Figure 15.5 presents the MINITAB output of a regression analysis of the real estate sales price data (see the page margin) using the model
y = β0 + β1x1 + β2x2 + β2x22 + e
FIGURE 15.5
MINITAB Output of a Regression Analysis of the Real Estate Sales Price Data Using the Model y = β0 + β1x1 + β2x2 + β3x22 + ε

a. Discuss why the plots of y versus x1 and y versus x2 in the page margin below the data indicate that this model might appropriately relate y to x1 and x2.
b. Do the p-values for the independent variables in this model indicate that these independent variables are significant? Explain your answer.
c. Report and interpret a point prediction of and a 95 percent prediction interval for the sales price of an individual house having 2.000 square feet and a rating of 8 (see the bottom of the MINITAB output in Figure 15.5).

Transcribed Image Text:

The regression equation is salesprice = 19.1 + 5.56 x1 + 9.22 x2-0.513 x2sq Predictor Constant 19.0743.632 5.25 0.002 x1 x2 x2sq Coef SE Coef 5.5596 0.1255 44.29 0.000 9.223 1.312 7.03 0.000 -0.5129 0.1228 -4.18 0.006 S 1.7712 8 R-Sq 99.7% R-Sq (adj ) -99.6% - Analysis of Variance Source Regression Residual Error 6 18.8 Total MS 3 7428.7 2476.2 789.25 0.000 3.1 DF 9 7447.5 Values of Predictors for New Obs Predicted Values for New observations New Obs xl x2 x2sq (169.095% 173.411) New Obs Fit SE Fit Fit 95% PI 95% CI 1 171.222 0.895 (169.033, 173.411) (166.367, 176.078) (166.367, 1 20.0 8.00 64.0 17