BELOW QUESTIONS ARE CONTINUOUS WITH ONE ANOTHER. PLEASE SOLVE ALL THE QUESTIONS.

PLEASE USE EXCEL AND SHOW ALL STEPS.

Q4 (A) 1B Develop a multiple regression model to predict the number of wins as a function of all numerical independent variable. In doing so, first start with developing a correlation matrix involving all numerical variable, including the response variable. Select the most promising independent variable and run regression to develop a regression model. In doing so, make sure to check the residual plots and probability curve so that you can test regression assumptions Test all four regression assumptions. (C) Wins E.R.A un Scor Saves Errors Alloweks Allowed 93 3.90 712 55 100 1433 48 69 4.70 734 35 101 1449 529 95 3.85 804 51 74 1401 431 90 3.19 697 50 114 1233 469 73 4.64 716 29 101 1439 574 85 4.02 748 37 70 1365 503 68 4.78 667 43 96 1503 543 88 3.75 726 40 99 1409 438 72 4.30 676 44 113 1504 542 66 4.77 701 35 107 1536 465 89 4.02 767 36 98 1339 483 94 3.48 713 47 111 1360 462 75 3.76 619 43 72 1359 449 55 4.56 583 31 118 1493 540 93 3.99 808 43 85 1378 446 94 3.42 700 47 86 1310 464 74 4.09 650 36 101 1368 488 81 3.83 684 42 101 1387 409 69 4.09 609 39 103 1448 495 98 3.33 731 51 94 1296 497 79 3.86 651 45 112 1357 490 88 3.71 765 42 107 1420 436 Q5(A) Add more promising variables to the model and develop the best regression model Why do you think its is the best model? (B) Assume some reasonable values for the independent variables in the above model confidence and prediction intervals. Explain what they mean. Q6 Add the size (categorical Variable) to the model developed in Q5. and run regression (Note: Before you do that, you will need to add two dummy variables as follows: Is this categorical variable significant for predicting the number of wins? If, So Interpret the coefficients. Small Medium Large d1 0 0 1 d2 0 1 0 Q7 Based on the various relevant factors, one wants to predict if a team is going to have a winning season. Develop a logistic regression for this purpose. Use the knowledge that you gained in Q5 in selecting the variables. Once you have developed a logistic regression model. Assume some values of the variables in the model and calculate the probability pf a team with all those values to have a winning season. Q4 (A) 1B Develop a multiple regression model to predict the number of wins as a function of all numerical independent variable. In doing so, first start with developing a correlation matrix involving all numerical variable, including the response variable. Select the most promising independent variable and run regression to develop a regression model. In doing so, make sure to check the residual plots and probability curve so that you can test regression assumptions Test all four regression assumptions. (C) Wins E.R.A un Scor Saves Errors Alloweks Allowed 93 3.90 712 55 100 1433 48 69 4.70 734 35 101 1449 529 95 3.85 804 51 74 1401 431 90 3.19 697 50 114 1233 469 73 4.64 716 29 101 1439 574 85 4.02 748 37 70 1365 503 68 4.78 667 43 96 1503 543 88 3.75 726 40 99 1409 438 72 4.30 676 44 113 1504 542 66 4.77 701 35 107 1536 465 89 4.02 767 36 98 1339 483 94 3.48 713 47 111 1360 462 75 3.76 619 43 72 1359 449 55 4.56 583 31 118 1493 540 93 3.99 808 43 85 1378 446 94 3.42 700 47 86 1310 464 74 4.09 650 36 101 1368 488 81 3.83 684 42 101 1387 409 69 4.09 609 39 103 1448 495 98 3.33 731 51 94 1296 497 79 3.86 651 45 112 1357 490 88 3.71 765 42 107 1420 436 Q5(A) Add more promising variables to the model and develop the best regression model Why do you think its is the best model? (B) Assume some reasonable values for the independent variables in the above model confidence and prediction intervals. Explain what they mean. Q6 Add the size (categorical Variable) to the model developed in Q5. and run regression (Note: Before you do that, you will need to add two dummy variables as follows: Is this categorical variable significant for predicting the number of wins? If, So Interpret the coefficients. Small Medium Large d1 0 0 1 d2 0 1 0 Q7 Based on the various relevant factors, one wants to predict if a team is going to have a winning season. Develop a logistic regression for this purpose. Use the knowledge that you gained in Q5 in selecting the variables. Once you have developed a logistic regression model. Assume some values of the variables in the model and calculate the probability pf a team with all those values to have a winning season