ISYE 6414 Final Exam Questions and Answers | 100% Pass

ISYE 6414 Final Exam Questions and Answers | 100% Pass 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. - Answer️️ -True 2. Penalization in linear regression models means penalizing for complex models, that is, models with a large number of predictors. - Answer️️ -True 3. Elastic net regression uses both penalties of the ridge and lasso regression and hence combines the benefits of both. - Answer️️ -True 4. Variable selection can be applied to regression problems when the number of pre- dicting variables is larger than the number of observations. - Answer️️ -True 5. The lasso regression performs well under multicollineariy. - Answer️️ -False 6. The selected variables using best subset regression are the best ones in explaining and predicting the response variables. - Answer️️ -False 8. The lasso regression requires a numerical algorithm to minimize the penalized sum of least squares. - Answer️️ -True 9. An unbiased estimator of the prediction risk is the training risk. - Answer️️ -False Page 2 of 4 10. Backward and forward stepwise regression will generally provide different sets of selected variables when p, the number of predicting variables, is large. - Answer️️ - True 11. All regularized regression approaches can be used for variable selection. - Answer️️ -False 12. Before performing regularized regression, we need to standardize or rescale the pre- dicting variables. - Answer️️ -True 13. The larger the number of predicting variables is, the larger the bias but the smaller the variance is. - Answer️️ -False 14. Variable selection is a simple and solved statistical problem since we can implement it using the R statistical software. - Answer️️ -False 15. BIC penalizes for complexity of the model more than AIC or Mallow's Cp statistics. - Answer️️ -True 16. The penalty constant λ in penalized or regularized regression controls the trade-off between lack of fit and model complexity. - Answer️️ -True 17. We cannot perform variable selection based on the statistical significance of the regression coefficients. - Answer️️ -True 18. Akaike Information Criterion is an estimate for the prediction risk. - Answer️️ -True 19. Forward stepwise regression is a greedy algorithm searching through all possible combinations of the predicting variables. - Answer️️ -False 20. Forward stepwise regression is preferable over backward stepwise regression because it starts with smaller models. - Answer️️ -True