Case Study: Consider the data set for Exercise 12.12, page

Chapter 12, Problem 12.72

(choose chapter or problem)

Case Study: Consider the data set for Exercise 12.12, page 452 (hospital data), repeated here. Site x1 x2 x3 x4 x5 y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 15.57 44.02 20.42 18.74 49.20 44.92 55.48 59.28 94.39 128.02 96.00 131.42 127.21 252.90 409.20 463.70 510.22 2463 2048 3940 6505 5723 11,520 5779 5969 8461 20,106 13,313 10,771 15,543 36,194 34,703 39,204 86,533 472.92 1339.75 620.25 568.33 1497.60 1365.83 1687.00 1639.92 2872.33 3655.08 2912.00 3921.00 3865.67 7684.10 12,446.33 14,098.40 15,524.00 18.0 9.5 12.8 36.7 35.7 24.0 43.3 46.7 78.7 180.5 60.9 103.7 126.8 157.7 169.4 331.4 371.6 4.45 6.92 4.28 3.90 5.50 4.60 5.62 5.l5 6.18 6.15 5.88 4.88 5.50 7.00 10.75 7.05 6.35 566.52 696.82 1033.15 1003.62 1611.37 1613.27 1854.17 2160.55 2305.58 3503.93 3571.59 3741.40 4026.52 10,343.81 11,732.17 15,414.94 18,854.45(a) The SAS PROC REG outputs provided in Figures 12.9 and 12.10 supply a considerable amount of information. Goals are to do outlier detection and eventually determine which model terms are to be used in the nal model. (b) Often the role of a single regressor variable is not apparent when it is studied in the presence of several other variables. This is due to multicollinearity. With this in mind, comment on the importance of x2 and x3 in the full model as opposed to their importance in a model in which they are the only variables. (c) Comment on what other analyses should be run. (d) Run appropriate analyses and write your conclusions concerning the nal model.