ISYE 6501 Final Exam Actual 2026/2027 – Complete Q&A with
Detailed Rationales | 100% Verified | Pass Guaranteed – A+
Graded
1. What do descriptive questions ask?
A) What will happen?
B) What action(s) would be best?
C) What happened?
D) Why did it happen?
Answer: C
Rationale: Descriptive questions focus on understanding what has already occurred—summarizing past
data to gain insights. Examples include "which customers are most alike?" or "what were last quarter's
sales?" This contrasts with predictive questions (what will happen?) and prescriptive questions (what
action would be best?).
2. What do predictive questions ask?
A) What happened?
B) What will happen?
C) What action would be best?
D) Which customers are most alike?
Answer: B
Rationale: Predictive questions focus on forecasting future outcomes using historical data and models.
Examples include "what will Google's stock price be tomorrow?" or "which customers are likely to
churn?".
,3. What do prescriptive questions ask?
A) What happened?
B) What will happen?
C) What action(s) would be best?
D) When did it happen?
Answer: C
Rationale: Prescriptive questions seek to identify optimal actions using optimization and simulation
techniques. Examples include "where should we place traffic lights?" or "what price should we set?".
4. What is a model in the context of analytics?
A) A computer program
B) A reallife situation expressed as mathematics
C) A data visualization
D) A type of algorithm
Answer: B
Rationale: A model is a mathematical representation of a reallife situation that captures key
relationships, allowing analysis, prediction, and optimization without manipulating the real system.
,5. Which of the following is NOT one of the three pillars of analytics?
A) Descriptive
B) Predictive
C) Prescriptive
D) Diagnostic
Answer: D
Rationale: The three pillars of analytics are descriptive (what happened?), predictive (what will
happen?), and prescriptive (what action would be best?). Diagnostic is not one of the three core pillars.
6. What is a model's purpose in analytics?
A) To memorize data exactly
B) To mathematically explain a realworld situation
C) To replace human decisionmaking
D) To collect more data
Answer: B
Rationale: Modeling is a way to mathematically explain a realworld situation so that we can understand
why something happened (or will happen) and what we can do about it.
7. What does R² measure in a regression model?
A) Error rate
B) Model complexity
, C) Variance explained
D) Bias
Answer: C
Rationale: R² indicates the proportion of the variance in the dependent variable that is explained by the
independent variables. It ranges from 0 to 1, with higher values indicating a better fit.
8. What is overfitting?
A) Underestimating error
B) Model too simple
C) Model fits noise
D) Data cleaning
Answer: C
Rationale: Overfitting occurs when a model captures random noise and fluctuations in the training data
instead of the true underlying signal, leading to poor generalization on new data.
9. What is underfitting?
A) Model fits training data perfectly
B) Model is too complex
C) Model is too simple to capture underlying patterns
D) Model has too many parameters
Detailed Rationales | 100% Verified | Pass Guaranteed – A+
Graded
1. What do descriptive questions ask?
A) What will happen?
B) What action(s) would be best?
C) What happened?
D) Why did it happen?
Answer: C
Rationale: Descriptive questions focus on understanding what has already occurred—summarizing past
data to gain insights. Examples include "which customers are most alike?" or "what were last quarter's
sales?" This contrasts with predictive questions (what will happen?) and prescriptive questions (what
action would be best?).
2. What do predictive questions ask?
A) What happened?
B) What will happen?
C) What action would be best?
D) Which customers are most alike?
Answer: B
Rationale: Predictive questions focus on forecasting future outcomes using historical data and models.
Examples include "what will Google's stock price be tomorrow?" or "which customers are likely to
churn?".
,3. What do prescriptive questions ask?
A) What happened?
B) What will happen?
C) What action(s) would be best?
D) When did it happen?
Answer: C
Rationale: Prescriptive questions seek to identify optimal actions using optimization and simulation
techniques. Examples include "where should we place traffic lights?" or "what price should we set?".
4. What is a model in the context of analytics?
A) A computer program
B) A reallife situation expressed as mathematics
C) A data visualization
D) A type of algorithm
Answer: B
Rationale: A model is a mathematical representation of a reallife situation that captures key
relationships, allowing analysis, prediction, and optimization without manipulating the real system.
,5. Which of the following is NOT one of the three pillars of analytics?
A) Descriptive
B) Predictive
C) Prescriptive
D) Diagnostic
Answer: D
Rationale: The three pillars of analytics are descriptive (what happened?), predictive (what will
happen?), and prescriptive (what action would be best?). Diagnostic is not one of the three core pillars.
6. What is a model's purpose in analytics?
A) To memorize data exactly
B) To mathematically explain a realworld situation
C) To replace human decisionmaking
D) To collect more data
Answer: B
Rationale: Modeling is a way to mathematically explain a realworld situation so that we can understand
why something happened (or will happen) and what we can do about it.
7. What does R² measure in a regression model?
A) Error rate
B) Model complexity
, C) Variance explained
D) Bias
Answer: C
Rationale: R² indicates the proportion of the variance in the dependent variable that is explained by the
independent variables. It ranges from 0 to 1, with higher values indicating a better fit.
8. What is overfitting?
A) Underestimating error
B) Model too simple
C) Model fits noise
D) Data cleaning
Answer: C
Rationale: Overfitting occurs when a model captures random noise and fluctuations in the training data
instead of the true underlying signal, leading to poor generalization on new data.
9. What is underfitting?
A) Model fits training data perfectly
B) Model is too complex
C) Model is too simple to capture underlying patterns
D) Model has too many parameters
