ISYE 6414 FINAL EXAM QUESTIONS AND VERIFIED CORRECT ANSWERS 2024/2025

ISYE 6414 FINAL EXAM QUESTIONS AND
VERIFIED CORRECT ANSWERS 2024/2025
Least *Square *Elimination *(LSE) *cannot *be *applied *to *GLM *models. *- *CORRECT *ANSWER-
False *- *it *is *applicable *but *does *not *use *data *distribution *information *fully.



In *multiple *linear *regression *with *idd *and *equal *variance, *the *least *squares *estimation *of
*regression *coefficients *are *always *unbiased. *- *CORRECT *ANSWER-True *- *the *least *squares
*estimates *are *BLUE *(Best *Linear *Unbiased *Estimates) *in *multiple *linear *regression.



Maximum *Likelihood *Estimation *is *not *applicable *for *simple *linear *regression *and *multiple
*linear *regression. *- *CORRECT *ANSWER-False *- *In *SLR *and *MLR, *the *SLE *and *MLE *are
*the *same *with *normal *idd *data.



The *backward *elimination *requires *a *pre-set *probability *of *type *II *error *- *CORRECT
*ANSWER-False *- *Type *I *error



The *first *degree *of *freedom *in *the *F *distribution *for *any *of *the *three *procedures *in
*stepwise *is *always *equal *to *one. *- *CORRECT *ANSWER-True



MLE *is *used *for *the *GLMs *for *handling *complicated *link *function *modeling *in *the *X-Y
*relationship. *- *CORRECT *ANSWER-True



In *the *GLMs *the *link *function *cannot *be *a *non *linear *regression. *- *CORRECT *ANSWER-
False *- *It *can *be *linear, *non *linear, *or *parametric



When *the *p-value *of *the *slope *estimate *in *the *SLR *is *small *the *r-squared *becomes
*smaller *too. *- *CORRECT *ANSWER-False *- *When *P *value *is *small, *the *model *fits *become
*more *significant *and *R *squared *become *larger.



In *GLMs *the *main *reason *one *does *not *use *LSE *to *estimate *model *parameters *is *the
*potential *constrained *in *the *parameters. *- *CORRECT *ANSWER-False *- *The *potential
*constraint *in *the *parameters *of *GLMs *is *handled *by *the *link *function.

, The *R-squared *and *adjusted *R-squared *are *not *appropriate *model *comparisons *for *non
*linear *regression *but *are *for *linear *regression *models. *- *CORRECT *ANSWER-TRUE *- *The
*underlying *assumption *of *R-squared *calculations *is *that *you *are *fitting *a *linear *model.



The *decision *in *using *ANOVA *table *for *testing *whether *a *model *is *significant *depends
*on *the *normal *distribution *of *the *response *variable *- *CORRECT *ANSWER-True



When *the *data *may *not *be *normally *distributed, *AIC *is *more *appropriate *for *variable
*selection *than *adjusted *R-squared *- *CORRECT *ANSWER-True



The *slope *of *a *linear *regression *equation *is *an *example *of *a *correlation *coefficient. *-
*CORRECT *ANSWER-False *- *the *correlation *coefficient *is *the *r *value. *Will *have *the *same
*+ *or *- *sign *as *the *slope.



In *multiple *linear *regression, *as *the *value *of *R-squared *increases, *the *relationship

between *predictors *becomes *stronger *- *CORRECT *ANSWER-False *- *r *squared *measures *how
*much *variability *is *explained *by *the *model, *NOT *how *strong *the *predictors *are.



When *dealing *with *a *multiple *linear *regression *model, *an *adjusted *R-squared *can

be *greater *than *the *corresponding *unadjusted *R-Squared *value. *- *CORRECT *ANSWER-False *-
*the *adjusted *rsquared *value *take *the *number *and *types *of *predictors *into *account. *It *is
*lower *than *the *r *squared *value.



In *a *multiple *regression *problem, *a *quantitative *input *variable *x *is *replaced *by *x *−

mean(x). *The *R-squared *for *the *fitted *model *will *be *the *same *- *CORRECT *ANSWER-True



The *estimated *coefficients *of *a *regression *line *is *positive, *when *the *coefficient *of

determination *is *positive. *- *CORRECT *ANSWER-False *- *r *squared *is *always *positive.



If *the *outcome *variable *is *quantitative *and *all *explanatory *variables *take *values *0 *or