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ISYE 6414 Final Exam Study Guide with Complete Solutions
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ISYE 6414 Final Exam Study Guide with Complete Solutions 1. If there are variables that need to be used to control the bias selection in the model, they should forced to be in the model and not being part of the variable selection process. - Answer-True 2. Penalization in linear regression mode...

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  • October 6, 2024
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  • 2024/2025
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EMILLYCHARLOTTE 2024/2025 ACADEMIC YAER ©2024 EMILLYCHARLOTTE. ALL RIGHTS RESERVED
FIRST PUBLISH SEPTEMBER 2024




ISYE 6414 Final Exam Study Guide with
Complete Solutions

1. If there are variables that need to be used to control the bias selection in the model, they should

forced to be in the model and not being part of the variable selection process. - Answer✔✔-True


2. Penalization in linear regression models means penalizing for complex models, that is, models with a

large number of predictors. - Answer✔✔-True


3. Elastic net regression uses both penalties of the ridge and lasso regression and hence combines the

benefits of both. - Answer✔✔-True


4. Variable selection can be applied to regression problems when the number of pre- dicting variables is

larger than the number of observations. - Answer✔✔-True


5. The lasso regression performs well under multicollineariy. - Answer✔✔-False


6. The selected variables using best subset regression are the best ones in explaining and predicting the

response variables. - Answer✔✔-False


8. The lasso regression requires a numerical algorithm to minimize the penalized sum of least squares. -

Answer✔✔-True


9. An unbiased estimator of the prediction risk is the training risk. - Answer✔✔-False


10. Backward and forward stepwise regression will generally provide different sets of selected variables

when p, the number of predicting variables, is large. - Answer✔✔-True

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