x1 x2 x3 x4 y 1 0.7397 0.5182 0.5921 0.6813 6.7698 2 0.0513 0.2692 0.3129 0.0963 2.6009 3 0.7418 0.8251 0.7989 0.7003 7.5796 4 0.7726 0.3167 0.6350 0.7100 5.9081 5 0.9787 0.8464 0.8611 0.8832 9.1086 6 0.2486 0.2441 0.7144 0.2321 3.2470 7 0.6101 0.5158 0.4233 0.5465 5.9483 8 0.1065 0.5420 0.5007 0.1760 3.1956 9 0.2411 0.0656 0.2759 0.2492 2.7149 10 0.0676 0.3367 0.5687 0.1367 3.6712 11 0.1701 0.7204 0.5543 0.2752 3.8920 12 0.3062 0.0476 0.3009 0.2552 2.7120 13 0.1776 0.0569 0.8435 0.2185 4.5517 14 0.5220 0.1466 0.0671 0.4459 4.5685 15 0.3764 0.6594 0.5615 0.4632 4.6490 16 0.6496 0.4242 0.2730 0.5984 5.7855 17 0.3446 0.1597 0.5718 0.3367 3.9235 18 0.1182 0.0902 0.2435 0.2008 2.4047 19 0.0756 0.6914 0.1508 0.1381 2.4687 20 0.2076 0.5350 0.6786 0.2981 4.3649 21 0.6412 0.0085 0.1172 0.6131 5.3632 22 0.0209 0.5795 0.5289 0.0827 2.1870 23 0.1187 0.6261 0.8847 0.1656 4.6336 24 0.1135 0.6330 0.9938 0.2445 5.3560 25 0.2738 0.3704 0.2663 0.3430 3.5795 26 0.9316 0.9040 0.2575 0.9043 7.8282 27 0.0796 0.8744 0.5268 0.2356 3.7082 28 0.8423 0.6059 0.3166 0.7571 7.4629 29 0.3521 0.9666 0.3153 0.3994 4.1417 30 0.4944 0.1678 0.8461 0.4653 5.6264 31 0.7522 0.2580 0.9788 0.6285 7.6010 32 0.3548 0.8739 0.1581 0.3725 4.8256 33 0.8969 0.4785 0.1750 0.8258 6.7364 34 0.0471 0.2116 0.1919 0.1067 1.3268 35 0.2909 0.6615 0.3866 0.3752 4.2733 36 0.9332 0.3705 0.6457 0.8466 7.8295 37 0.1781 0.5727 0.4649 0.2536 3.4103 38 0.4595 0.4325 0.2328 0.4350 4.6200 39 0.7086 0.4575 0.5898 0.6208 6.4299 40 0.7556 0.0029 0.8807 0.6767 6.1042 41 0.0424 0.1800 0.6058 0.1109 3.4765 42 0.4466 0.3186 0.7078 0.4401 4.7803 43 0.8163 0.4930 0.6268 0.7778 6.6354 44 0.9921 0.3285 0.1617 0.8631 6.1130 45 0.9808 0.4697 0.4952 0.9182 8.2968 46 0.8982 0.4220 0.4164 0.7652 6.4015 47 0.8141 0.0714 0.4935 0.6719 5.8090 48 0.6719 0.6817 0.3007 0.6197 5.6423 49 0.0759 0.2451 0.7491 0.1360 2.4426 50 0.9153 0.8381 0.4439 0.8440 6.9311

Consider the data set tass4_ql.txt. (a) Generate the correlation matrix and see if there are any strong correlation between the independent variables. (b) Fit the data to the first order model. Do the F-test in the ANOVA table and the individual t-tests suggest multicollinearity? (c) Do the VIF values suggest multicollinearity? State the rule of thumb and check the VIF values. (d) Do the conditional indices and proportions of variation suggest multicollinearity? State the rule of thumb and check the indices and proportions. (e) Plot the ridge trace in which the ridge parameter goes from 0.0 to 0.2 by 0.005. (f) From the ridge trace and the VIF values, suggest a value for the ridge parameter. Explain your choice. (g) Report the parameter estimates of the ridge regression with the parameter you chose above. Consider the data set tass4_ql.txt. (a) Generate the correlation matrix and see if there are any strong correlation between the independent variables. (b) Fit the data to the first order model. Do the F-test in the ANOVA table and the individual t-tests suggest multicollinearity? (c) Do the VIF values suggest multicollinearity? State the rule of thumb and check the VIF values. (d) Do the conditional indices and proportions of variation suggest multicollinearity? State the rule of thumb and check the indices and proportions. (e) Plot the ridge trace in which the ridge parameter goes from 0.0 to 0.2 by 0.005. (f) From the ridge trace and the VIF values, suggest a value for the ridge parameter. Explain your choice. (g) Report the parameter estimates of the ridge regression with the parameter you chose above