The Ridge regression coefficients and the VIFS of the Body Fat example are given below. Find the best biasing constant and the original regression equation. Use the following data for the body-fat example: where == 20.195, X = 25.305, X2 = 51.170, X3 === 82 = 5.235, and $3 = 3.647. TABLE 11.2 Ridge Estimated Standardized Regression Coefficients for Different Biasing Constants c-Body Fat Example with Three Predictor Variables. === 27.620, sy 5.106, S = 5.023, TABLE 11.3 VIF Values for Regression Coefficients and R2 for Different Biasing Constants c-Body Fat Example with Three Predictor Variables. bR b bR C (VIF)1 (VIF)2 (VIF)3 R .000 4.264 -2.929 -1.561 .000 708.84 564.34 104.61 .8014 .002 1.441 -.4113 -.4813 .002 50.56 40.45 8.28 .7901 .004 1.006 -.0248 -.3149 .004 16.98 13.73 3.36 .7864 .006 .8300 .1314 -.2472 .006 8.50 6.98 2.19 .7847 .008 .7343 .2158 -.2103 .008 5.15 4.30 1.62 .7838 .010 .6742 .2684 -.1870 .010 3.49 2.98 1.38 .7832 .020 .5463 .3774 -.1369 .020 1.10 1.08 1.01 .7818 .030 .5004 .4134 -.1181 .030 .63 .70 .92 .7812 .040 .4760 .4302 -.1076 .040 .45 .56 .88 .7808 .050 .4605 .4392 -.1005 .050 .37 .49 .85 .7804 .100 .4234 .4490 -.0812 .100 .25 .37 .76 .7784 .500 .3377 .3791 -.0295 .500 .15 .21 .40 .7427 1.000 .2798 .3101 -.0059 1.000 .11 .14 .23 .6818