1 Consider the data given in table 4 Augment the data by the dummy variable cooling tower as given in Table 6 a Build a multiple regression model to predict ln (C) by taking S and N or their natural logarithms, as well as CT, as the independent variables Make sure to check for multicollinearity b Use residual analysis and R 2 to check your model c State which variables are important in predicting the cost of constructing an LWR plant d State a prediction equation that can be used to predict ln(C) e Does adding CT improve R 2 If so, by what amount 2 Consider the data in Table 5 once more a Evaluate the correlation between the two scores and state if there seems to be any association between the two b Find the probability of upgrading for each division of the sample by the Bayes' theorem c Find the probability of upgrading for each division of the sample by the nave version of the Bayes' theorem d Compare your results in parts b and c and explain the difference or indifference based on observed probabilities Please provide your work in detail and include in text citations Results on the Tests Taken by Employees Table 4 Table 6 Group Test Test 2 Cooling Tower Data in Constructing Light Water Reactors 90 85 Data Concerning Construction of Light Water Reactors 96 38 91 81 Plant C Plant S N CT 1 0 95 78 460 05 687 14 2 92 85 2 452 99 1,065 3 93 87 3 443 22 1,065 98 84 652 32 1,065 12 92 5 642 23 1 065 12 6 345 39 514 3 272 37 822 317 21 457 457 12 822 10 690 19 792 11 350 63 560 12 402 59 790 13 412 18 530 14 495 58 1,050 15 394 36 850 16 423 32 778 17 712 27 845 17 18 289 66 530 OOOOOOOOOOOOOOOOOOOOHHHHHHHH HH HH HHHH HH HMM P P HOHHOOHHOHOHOOHOOOOHOOHOOOH 10 881 24 1 090 20 490 88 1,050 8 21 567 79 913 15 22 665 99 828 20 23 621 45 786 18 24 608 8 821 3 25 473 64 538 10 26 697 14 1,130 21 27 207 51 745 8 28 288 48 821 29 284 88 886 11 30 280 36 886 11 31 217 38 745 8 32 32 270 71 886 11

Question

1 Consider the data given in table 4  Augment the data by the dummy variable  cooling tower  as given in Table 6  a Build a multiple regression model to predict ln (C) by taking S and N or their natural logarithms, as well as CT, as the independent variables  Make sure to check for multicollinearity  b Use residual analysis and R 2 to check your model  c State which variables are important in predicting the cost of constructing an LWR plant  d State a prediction equation that can be used to predict ln(C)  e Does adding CT improve R 2   If so, by what amount  2  Consider the data in Table 5 once more  a Evaluate the correlation between the two scores and state if there seems to be any association between the two  b Find the probability of upgrading for each division of the sample by the Bayes' theorem  c Find the probability of upgrading for each division of the sample by the nave version of the Bayes' theorem  d Compare your results in parts b and c and explain the difference or indifference based on observed probabilities  Please provide your work in detail and include in text citations  Results on the Tests Taken by Employees Table 4 Table 6 Group Test Test 2 Cooling Tower Data in Constructing Light Water Reactors 90 85 Data Concerning Construction of Light Water Reactors 96 38 91 81 Plant C Plant S N CT 1 0 95 78 460 05 687 14 2 92 85 2 452 99 1,065 3 93 87 3 443 22 1,065 98 84 652 32 1,065 12 92 5 642 23 1 065 12 6 345 39 514 3 272 37 822 317 21 457 457 12 822 10 690 19 792 11 350 63 560 12 402 59 790 13 412 18 530 14 495 58 1,050 15 394 36 850 16 423 32 778 17 712 27 845 17 18 289 66 530 OOOOOOOOOOOOOOOOOOOOHHHHHHHH HH HH HHHH HH HMM P P HOHHOOHHOHOHOOHOOOOHOOHOOOH 10 881 24 1 090 20 490 88 1,050 8 21 567 79 913 15 22 665 99 828 20 23 621 45 786 18 24 608 8 821 3 25 473 64 538 10 26 697 14 1,130 21 27 207 51 745 8 28 288 48 821 29 284 88 886 11 30 280 36 886 11 31 217 38 745 8 32 32 270 71 886 11

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1.Consider the data given in table 4. Augment the data by the dummy variable cooling tower as given in Table 6. a.Build a multiple regression

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