Multicollinearity in real estate data. D. Hamilton illustrated the multicollinearity problem with an example using the data shown in the accompany-ing table. The values of x1,x2,and y in the table at right represent appraised land value, appraised improvements value, and sale price, respectively, of a randomly selected residential property. (All measurements are in thousands of dollars.) (a) Calculate the coefficient of correlation between y and x1. Is there evidence of a linear rela-tionship between sale price and appraised land value? (b) Calculate the coefficient of correlation between y and x2. Is there evidence of a linear rela-tionship between sale price and appraised improvements? (c) Based on the results in parts a and b, do you think the model E(y) = 0 + 1x1 + 2x2 will be useful for predicting sale price? x1 HAMILTON x2 y 22.3 96.6 123.7 25.7 89.4 126.6 38.7 44.0 120.0 31.0 66.4 119.3 33.9 49.1 110.6 28.3 85.2 130.3 30.2 80.4 131.3 x1 x2 y 30.4 77.1 128.6 32.6 51.1 108.4 33.9 50.5 112.0 23.5 85.1 115.6 27.6 65.9 108.3 39.0 49.0 126.3 31.6 69.6 124.6 21.4 90.5 114.4