ISYE 6414 FINAL EXAM 100% APPROVED Q
& A Latest 2024
Variable *selection *can *be *used *to *deal *with *multicollinearity, *reduce *predictors *and *fit *a
*model *with *more *variables *than *observations. *- *CORRECT *ANSWER-True
Variable *selection *is *solved *statistical *problem, *particularly *for *a *models *with *a *large
*number *of *predictors. *- *CORRECT *ANSWER-False
When *selecting *variables, *we *need *to *first *establish *which *variables *are *used *for
*controlling *bias *selection *in *the *sample *and *which *are *explanatory. *- *CORRECT *ANSWER-
True
The *selected *variables *using *variable *selection *approaches *are *the *only *variables *that
*explain *the *response *variable. *- *CORRECT *ANSWER-False
If *a *predicting *variables *is *selected *to *be *in *the *model, *we *conclude *that *the *predicting
*variable *has *a *causal *relationship *with *the *response *variable. *- *CORRECT *ANSWER-False
Variable *selection *methods *is *performed *by *- *CORRECT *ANSWER-Balancing *the *bias-variance
*trade-off.
There *are *multiple *model *selection *criteria *that *can *be *used *and *all *provide *the *same
*penalization *of *the *model *complexity. *- *CORRECT *ANSWER-False
The *AIC *is *commonly *used *for *prediction *models *since *it *penalizes *the *model *complexity
*the *most. *- *CORRECT *ANSWER-False
The *AIC *and *BIC *cannot *be *used *in *selecting *variables *for *generalized *linear *models. *-
*CORRECT *ANSWER-False
, A *model *selection *method *that *can *be *performed *in *the *R *statistical *software *given *a
*set *of *controlling *factors *that *form *the *minimum *or *starting *model. *- *CORRECT
*ANSWER-Stepwise *regression
Forward *stepwise *regression *adds *one *variable *to *the *model *at *a *time *starting *with *a
*minimum *model. *- *CORRECT *ANSWER-True
Backward *stepwise *regression *adds *one *variable *to *the *model *at *a *time *starting *with
*the *full *model. *- *CORRECT *ANSWER-False
It *is *always *feasible *to *apply *a *model *search *for *all *possible *combinations *of *the
*predicting *variables. *- *CORRECT *ANSWER-False
The *advantage *of *having *a *biased *model *with *less *predicting *variables *is *the *reduction
*in *uncertainty *of *predictions *of *future *responses. *- *CORRECT *ANSWER-True
Complexity *is *equivalent *to *a *large *model *with *many *predicting *variables. *- *CORRECT
*ANSWER-True
The *goal *is *to *balance *the *trade-off *between *bias *(more *predictors) *and *variance *(fewer
*predictors). *- *CORRECT *ANSWER-True
We *can *combine *ridge *and *lasso *regression *into *what *we *call *the *elastic *net *regression.
*- *CORRECT *ANSWER-True
Ridge *regression *is *used *for *variable *selection. *- *CORRECT *ANSWER-False
The *penalty *for *lasso *regression *is *not *a *sparsity *penalty. *- *CORRECT *ANSWER-False
All *regularized *regression *approaches *will *select *the *same *model. *- *CORRECT *ANSWER-
False
& A Latest 2024
Variable *selection *can *be *used *to *deal *with *multicollinearity, *reduce *predictors *and *fit *a
*model *with *more *variables *than *observations. *- *CORRECT *ANSWER-True
Variable *selection *is *solved *statistical *problem, *particularly *for *a *models *with *a *large
*number *of *predictors. *- *CORRECT *ANSWER-False
When *selecting *variables, *we *need *to *first *establish *which *variables *are *used *for
*controlling *bias *selection *in *the *sample *and *which *are *explanatory. *- *CORRECT *ANSWER-
True
The *selected *variables *using *variable *selection *approaches *are *the *only *variables *that
*explain *the *response *variable. *- *CORRECT *ANSWER-False
If *a *predicting *variables *is *selected *to *be *in *the *model, *we *conclude *that *the *predicting
*variable *has *a *causal *relationship *with *the *response *variable. *- *CORRECT *ANSWER-False
Variable *selection *methods *is *performed *by *- *CORRECT *ANSWER-Balancing *the *bias-variance
*trade-off.
There *are *multiple *model *selection *criteria *that *can *be *used *and *all *provide *the *same
*penalization *of *the *model *complexity. *- *CORRECT *ANSWER-False
The *AIC *is *commonly *used *for *prediction *models *since *it *penalizes *the *model *complexity
*the *most. *- *CORRECT *ANSWER-False
The *AIC *and *BIC *cannot *be *used *in *selecting *variables *for *generalized *linear *models. *-
*CORRECT *ANSWER-False
, A *model *selection *method *that *can *be *performed *in *the *R *statistical *software *given *a
*set *of *controlling *factors *that *form *the *minimum *or *starting *model. *- *CORRECT
*ANSWER-Stepwise *regression
Forward *stepwise *regression *adds *one *variable *to *the *model *at *a *time *starting *with *a
*minimum *model. *- *CORRECT *ANSWER-True
Backward *stepwise *regression *adds *one *variable *to *the *model *at *a *time *starting *with
*the *full *model. *- *CORRECT *ANSWER-False
It *is *always *feasible *to *apply *a *model *search *for *all *possible *combinations *of *the
*predicting *variables. *- *CORRECT *ANSWER-False
The *advantage *of *having *a *biased *model *with *less *predicting *variables *is *the *reduction
*in *uncertainty *of *predictions *of *future *responses. *- *CORRECT *ANSWER-True
Complexity *is *equivalent *to *a *large *model *with *many *predicting *variables. *- *CORRECT
*ANSWER-True
The *goal *is *to *balance *the *trade-off *between *bias *(more *predictors) *and *variance *(fewer
*predictors). *- *CORRECT *ANSWER-True
We *can *combine *ridge *and *lasso *regression *into *what *we *call *the *elastic *net *regression.
*- *CORRECT *ANSWER-True
Ridge *regression *is *used *for *variable *selection. *- *CORRECT *ANSWER-False
The *penalty *for *lasso *regression *is *not *a *sparsity *penalty. *- *CORRECT *ANSWER-False
All *regularized *regression *approaches *will *select *the *same *model. *- *CORRECT *ANSWER-
False
