ACTUAL QUESTIONS AND ANSWERS
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. *- *CORRECT *ANSWER-True
2. *Penalization *in *linear *regression *models *means *penalizing *for *complex *models, *that *is,
*models *with *a *large *number *of *predictors. *- *CORRECT *ANSWER-True
3. *Elastic *net *regression *uses *both *penalties *of *the *ridge *and *lasso *regression *and *hence
*combines *the *benefits *of *both. *- *CORRECT *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. *- *CORRECT *ANSWER-True
5. *The *lasso *regression *performs *well *under *multicollineariy. *- *CORRECT *ANSWER-False
6. *The *selected *variables *using *best *subset *regression *are *the *best *ones *in *explaining
*and *predicting *the *response *variables. *- *CORRECT *ANSWER-False
8. *The *lasso *regression *requires *a *numerical *algorithm *to *minimize *the *penalized *sum *of
*least *squares. *- *CORRECT *ANSWER-True
9. *An *unbiased *estimator *of *the *prediction *risk *is *the *training *risk. *- *CORRECT *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. *- *CORRECT
*ANSWER-True