ISYE 6501 Introduction to Analytics Modeling
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Final Exam Prep | 200 Questions & Answers
--- --- --- --- --- --- --- ---
with Rationales ---
Academic Year: 2026/2027 | Georgia Tech
--- --- --- --- ---
OMSA Study Bundle --- ---
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--
SECTION---1:---MODEL---VALIDATION---&---SELECTION---(Questions---1-25)---
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Q1:---In---a---linear---regression---model,---what---does---the---p-value---for---a---coefficient---indicate?---
Answer:---The---probability---of---observing---the---coefficient---(or---more---extreme)---if---the---true---
coefficient---is---zero.---
Rationale:---A---low---p-value---(<0.05)---suggests---the---predictor---is---statistically---significant,---meaning---
there---is---evidence---that---the---predictor---has---a---non-zero---effect---on---the---response---variable---.---
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--
Q2:---Which---validation---method---is---most---computationally---efficient---for---large---datasets?---
Answer:---Validation---set---approach---(holdout).---
Rationale:---Uses---a---single---train-test---split,---requiring---less---computation---than---cross-validation---
methods---like---k-fold---which---must---fit---the---model---k---times---.---
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Q3:---In---k-nearest---neighbors---(k-NN),---what---happens---if---k---is---set---to---1?---
Answer:---The---model---overfits;---the---decision---boundary---is---highly---irregular.---
Rationale:---Low---k---captures---noise---in---the---training---data,---resulting---in---high---variance---and---poor---
generalization---to---new---data---.---
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Q4:---What---does---the---coefficient---of---determination---(R²)---represent---in---linear---regression?---
Answer:---The---proportion---of---variance---in---the---dependent---variable---explained---by---the---model.---
Rationale:---R²---quantifies---the---percentage---of---total---variation---in---the---response---variable---
accounted---for---by---predictors,---ranging---from---0---to---1---where---higher---values---indicate---better---fit---.---
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Q5:---Which---of---the---following---is---a---key---assumption---of---ordinary---least---squares---(OLS)---linear---
regression?---
Answer:---The---errors---(residuals)---are---homoscedastic---and---uncorrelated.---
Rationale:---Key---OLS---assumptions---include---linearity,---independence---of---errors,---homoscedasticity---
(constant---variance),---and---normality---of---errors---for---inference.---Predictors---do---not---need---to---be---
normally---distributed---.---
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--
Q6:---What---is---the---primary---purpose---of---splitting---data---into---training---and---testing---sets?---
Answer:---To---provide---an---unbiased---evaluation---of---the---final---model.---
Rationale:---The---testing---set---is---used---only---to---evaluate---the---final---model's---performance---on---
unseen---data,---providing---an---unbiased---estimate---of---how---the---model---generalizes---to---new---data---.---
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--
Q7:---In---k-fold---cross-validation,---what---is---the---general---purpose---of---the---process?---
Answer:---To---split---data---into---k---subsets,---using---each---subset---once---as---a---test---set---and---the---rest---for---
training.---
Rationale:---This---provides---multiple---train-test---splits---to---better---estimate---model---performance---
and---reduce---variance---in---performance---estimates---.---
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--
Q8:---What---is---overfitting---in---machine---learning?---
Answer:---When---a---model---performs---well---on---training---data---but---poorly---on---unseen---data.---
Rationale:---Overfitting---occurs---when---a---model---learns---the---training---data---too---well,---including---
noise---and---random---fluctuations,---leading---to---poor---generalization---.---
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Q9:---What---is---the---bias-variance---tradeoff?---
Answer:---Models---with---high---bias---tend---to---underfit,---while---models---with---high---variance---tend---to---
overfit.---
Rationale:---Simple---models---have---high---bias---(underfitting)---and---low---variance;---complex---models---
have---low---bias---and---high---variance---(overfitting).---The---goal---is---to---find---the---right---balance---.---
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--
Q10:---What---is---the---purpose---of---regularization---in---regression---models?---
Answer:---To---penalize---the---size---of---coefficients---and---reduce---overfitting.---
Rationale:---Regularization---adds---a---penalty---term---to---the---loss---function---to---constrain---coefficient---
magnitudes,---helping---reduce---overfitting---and---improve---generalization---.---
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--
Q11:---What---does---the---Akaike---Information---Criterion---(AIC)---balance---when---comparing---models?---
Answer:---Model---fit---and---model---complexity.---
Rationale:---AIC---balances---model---fit---(likelihood)---with---model---complexity---(number---of---
parameters),---penalizing---overly---complex---models---to---avoid---overfitting---.---
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--
Q12:---How---does---the---Bayesian---Information---Criterion---(BIC)---differ---from---AIC?---
Answer:---BIC---generally---penalizes---complexity---more---heavily---than---AIC.---
Rationale:---BIC---penalizes---model---complexity---more---than---AIC,---tending---to---select---simpler---
models,---especially---with---larger---sample---sizes---.---
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--
Q13:---What---is---a---common---disadvantage---of---k-fold---cross-validation?---
Answer:---It---can---be---computationally---expensive---for---large---datasets.---
Rationale:---K-fold---cross-validation---requires---fitting---the---model---k---times,---which---can---be---
computationally---intensive,---and---it---is---not---directly---applicable---to---time---series---data---without---
special---adaptation---.---
, -
--
Q14:---LASSO---regression---can---shrink---coefficients---exactly---to---zero.---True---or---False?---
Answer:---True.---
Rationale:---LASSO---uses---an---L1---penalty---which---can---produce---sparse---solutions,---shrinking---some---
coefficients---exactly---to---zero---for---feature---selection---.---
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--
Q15:---Ridge---regression---uses---which---type---of---penalty?---
Answer:---L2---penalty---(sum---of---squared---coefficients).---
Rationale:---Ridge---regression---penalizes---the---sum---of---squared---coefficients,---shrinking---coefficients---
toward---zero---but---not---exactly---to---zero,---which---helps---with---multicollinearity---.---
-
--
Q16:---Adjusted---R-squared---is---preferred---over---R-squared---because:---
Answer:---It---penalizes---the---use---of---too---many---variables.---
Rationale:---Adjusted---R-squared---is---a---variant---of---R²---that---encourages---simpler---models---by---
penalizing---the---addition---of---unnecessary---predictors---.---
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--
Q17:---What---is---the---primary---advantage---of---using---a---validation---set?---
Answer:---It---can---be---used---to---tune---hyperparameters---without---overfitting---to---the---test---set.---
Rationale:---A---validation---set---allows---hyperparameter---tuning---and---model---selection---without---
touching---the---final---test---set,---which---remains---for---unbiased---evaluation---.---
-
--
Q18:---What---is---the---consequence---of---choosing---too---many---variables---in---a---regression---model?---
Answer:---The---model---will---likely---overfit---and---have---high---variance.---
Rationale:---Including---too---many---predictors---increases---model---complexity,---leading---to---overfitting---
where---the---model---captures---noise---rather---than---true---patterns---.---
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--
--- --- --- --- --- ---
Final Exam Prep | 200 Questions & Answers
--- --- --- --- --- --- --- ---
with Rationales ---
Academic Year: 2026/2027 | Georgia Tech
--- --- --- --- ---
OMSA Study Bundle --- ---
-
--
SECTION---1:---MODEL---VALIDATION---&---SELECTION---(Questions---1-25)---
-
--
Q1:---In---a---linear---regression---model,---what---does---the---p-value---for---a---coefficient---indicate?---
Answer:---The---probability---of---observing---the---coefficient---(or---more---extreme)---if---the---true---
coefficient---is---zero.---
Rationale:---A---low---p-value---(<0.05)---suggests---the---predictor---is---statistically---significant,---meaning---
there---is---evidence---that---the---predictor---has---a---non-zero---effect---on---the---response---variable---.---
-
--
Q2:---Which---validation---method---is---most---computationally---efficient---for---large---datasets?---
Answer:---Validation---set---approach---(holdout).---
Rationale:---Uses---a---single---train-test---split,---requiring---less---computation---than---cross-validation---
methods---like---k-fold---which---must---fit---the---model---k---times---.---
-
--
Q3:---In---k-nearest---neighbors---(k-NN),---what---happens---if---k---is---set---to---1?---
Answer:---The---model---overfits;---the---decision---boundary---is---highly---irregular.---
Rationale:---Low---k---captures---noise---in---the---training---data,---resulting---in---high---variance---and---poor---
generalization---to---new---data---.---
, -
--
Q4:---What---does---the---coefficient---of---determination---(R²)---represent---in---linear---regression?---
Answer:---The---proportion---of---variance---in---the---dependent---variable---explained---by---the---model.---
Rationale:---R²---quantifies---the---percentage---of---total---variation---in---the---response---variable---
accounted---for---by---predictors,---ranging---from---0---to---1---where---higher---values---indicate---better---fit---.---
-
--
Q5:---Which---of---the---following---is---a---key---assumption---of---ordinary---least---squares---(OLS)---linear---
regression?---
Answer:---The---errors---(residuals)---are---homoscedastic---and---uncorrelated.---
Rationale:---Key---OLS---assumptions---include---linearity,---independence---of---errors,---homoscedasticity---
(constant---variance),---and---normality---of---errors---for---inference.---Predictors---do---not---need---to---be---
normally---distributed---.---
-
--
Q6:---What---is---the---primary---purpose---of---splitting---data---into---training---and---testing---sets?---
Answer:---To---provide---an---unbiased---evaluation---of---the---final---model.---
Rationale:---The---testing---set---is---used---only---to---evaluate---the---final---model's---performance---on---
unseen---data,---providing---an---unbiased---estimate---of---how---the---model---generalizes---to---new---data---.---
-
--
Q7:---In---k-fold---cross-validation,---what---is---the---general---purpose---of---the---process?---
Answer:---To---split---data---into---k---subsets,---using---each---subset---once---as---a---test---set---and---the---rest---for---
training.---
Rationale:---This---provides---multiple---train-test---splits---to---better---estimate---model---performance---
and---reduce---variance---in---performance---estimates---.---
-
--
Q8:---What---is---overfitting---in---machine---learning?---
Answer:---When---a---model---performs---well---on---training---data---but---poorly---on---unseen---data.---
Rationale:---Overfitting---occurs---when---a---model---learns---the---training---data---too---well,---including---
noise---and---random---fluctuations,---leading---to---poor---generalization---.---
, -
--
Q9:---What---is---the---bias-variance---tradeoff?---
Answer:---Models---with---high---bias---tend---to---underfit,---while---models---with---high---variance---tend---to---
overfit.---
Rationale:---Simple---models---have---high---bias---(underfitting)---and---low---variance;---complex---models---
have---low---bias---and---high---variance---(overfitting).---The---goal---is---to---find---the---right---balance---.---
-
--
Q10:---What---is---the---purpose---of---regularization---in---regression---models?---
Answer:---To---penalize---the---size---of---coefficients---and---reduce---overfitting.---
Rationale:---Regularization---adds---a---penalty---term---to---the---loss---function---to---constrain---coefficient---
magnitudes,---helping---reduce---overfitting---and---improve---generalization---.---
-
--
Q11:---What---does---the---Akaike---Information---Criterion---(AIC)---balance---when---comparing---models?---
Answer:---Model---fit---and---model---complexity.---
Rationale:---AIC---balances---model---fit---(likelihood)---with---model---complexity---(number---of---
parameters),---penalizing---overly---complex---models---to---avoid---overfitting---.---
-
--
Q12:---How---does---the---Bayesian---Information---Criterion---(BIC)---differ---from---AIC?---
Answer:---BIC---generally---penalizes---complexity---more---heavily---than---AIC.---
Rationale:---BIC---penalizes---model---complexity---more---than---AIC,---tending---to---select---simpler---
models,---especially---with---larger---sample---sizes---.---
-
--
Q13:---What---is---a---common---disadvantage---of---k-fold---cross-validation?---
Answer:---It---can---be---computationally---expensive---for---large---datasets.---
Rationale:---K-fold---cross-validation---requires---fitting---the---model---k---times,---which---can---be---
computationally---intensive,---and---it---is---not---directly---applicable---to---time---series---data---without---
special---adaptation---.---
, -
--
Q14:---LASSO---regression---can---shrink---coefficients---exactly---to---zero.---True---or---False?---
Answer:---True.---
Rationale:---LASSO---uses---an---L1---penalty---which---can---produce---sparse---solutions,---shrinking---some---
coefficients---exactly---to---zero---for---feature---selection---.---
-
--
Q15:---Ridge---regression---uses---which---type---of---penalty?---
Answer:---L2---penalty---(sum---of---squared---coefficients).---
Rationale:---Ridge---regression---penalizes---the---sum---of---squared---coefficients,---shrinking---coefficients---
toward---zero---but---not---exactly---to---zero,---which---helps---with---multicollinearity---.---
-
--
Q16:---Adjusted---R-squared---is---preferred---over---R-squared---because:---
Answer:---It---penalizes---the---use---of---too---many---variables.---
Rationale:---Adjusted---R-squared---is---a---variant---of---R²---that---encourages---simpler---models---by---
penalizing---the---addition---of---unnecessary---predictors---.---
-
--
Q17:---What---is---the---primary---advantage---of---using---a---validation---set?---
Answer:---It---can---be---used---to---tune---hyperparameters---without---overfitting---to---the---test---set.---
Rationale:---A---validation---set---allows---hyperparameter---tuning---and---model---selection---without---
touching---the---final---test---set,---which---remains---for---unbiased---evaluation---.---
-
--
Q18:---What---is---the---consequence---of---choosing---too---many---variables---in---a---regression---model?---
Answer:---The---model---will---likely---overfit---and---have---high---variance.---
Rationale:---Including---too---many---predictors---increases---model---complexity,---leading---to---overfitting---
where---the---model---captures---noise---rather---than---true---patterns---.---
-
--
