ISYE 6501 - Midterm 1 NEWEST 2025/2026
ACTUAL EXAM COMPLETE QUESTIONS AND
CORRECT DETAILED ANSWERS (VERIFIED
ANSWERS) |ALREADY GRADED A+||BRAND
NEW VERSION!!
1. What do descriptive questions ask?
a) What action(s) might be best?
b) What came about? (e.g., which clients are most alike)
c) What will take place?
d) How to optimize a process?
2. What do predictive questions ask?
a) What came about?
b) What will take place? (e.g., what will Google’s stock rate be?)
c) What action should be taken?
d) Why did it happen?
3. What do prescriptive questions ask?
a) What came about?
b) What will take place?
c) What action(s) might be best? (e.g., where to position traffic lights)
d) When did it happen?
4. What is a “version” (model) in analytics?
a) A software program
b) A real-life scenario expressed as math
c) A type of data
d) A prediction algorithm
5. What do classifiers help you do?
a) Predict continuous values
b) Differentiate
c) Cluster similar items
d) Reduce dimensions
,6. What is a soft classifier and when is it used?
a) A classifier that always separates data perfectly
b) A classifier that uses only linear boundaries
c) In cases where there is no line that separates all classified examples – we
minimize the number of mistakes
d) A classifier that never makes errors
7. What does it mean when the classifier/decision boundary is almost parallel
to the vertical x-axis?
a) The vertical feature is all that is needed
b) The horizontal feature is all that is needed
c) Both features are equally important
d) The classifier is overfit
8. What does it mean when the classifier/decision boundary is almost parallel
to the horizontal y-axis?
a) The horizontal feature is all that is needed
b) The vertical feature is all that is needed
c) Neither feature matters
d) The classifier is underfit
9. What is time-series data?
a) Data recorded at random intervals
b) The same data recorded over time, often at equal periods
c) Data that is not time-dependent
d) Cross-sectional data
10. What is quantitative data?
a) Numbers with meaning: higher means more, lower means less (e.g., age,
income, temperature)
b) Numbers without meaning (e.g., zip codes)
c) Non-numeric data
d) Binary data only
11. What is categorical data?
a) Numbers with meaning (e.g., temperature)
b) Numbers without meaning (e.g., zip codes), non-numeric (e.g., hair color),
or binary (e.g., male/female)
c) Continuous measurements
d) Time-stamped data
, 12. Which of these is time series data?
a) The average price of a house in the United States every year since 1820
b) The height of each professional basketball player in the NBA at the start of the
season
c) The brand of car each person drives
d) The zip code of survey respondents
13. Which of these is structured data?
a) The contents of someone’s Twitter feed
b) The amount of money in a person’s bank account
c) A video recording
d) An email body
14. What is structured data?
a) Data that is messy and unorganized
b) Data that can be stored in a structured manner (e.g., rows and columns)
c) Data that only contains images
d) Data that cannot be described easily
15. What is unstructured data?
a) Data stored in a relational database
b) Data that is not easily described and stored (e.g., written text, images,
video)
c) Numerical data only
d) Data with a fixed schema
16. A survey of 25 people recorded each person’s family size and type of car.
Which of these is a data point?
a) The 14th person’s family size and car type
b) The 14th person’s family size only
c) The car type of everyone
d) The average family size
17. The farther the wrongly classified point is from the line …
a) The smaller the error
b) The bigger the error we have made
c) The more confident the classifier is
d) The less it matters
ACTUAL EXAM COMPLETE QUESTIONS AND
CORRECT DETAILED ANSWERS (VERIFIED
ANSWERS) |ALREADY GRADED A+||BRAND
NEW VERSION!!
1. What do descriptive questions ask?
a) What action(s) might be best?
b) What came about? (e.g., which clients are most alike)
c) What will take place?
d) How to optimize a process?
2. What do predictive questions ask?
a) What came about?
b) What will take place? (e.g., what will Google’s stock rate be?)
c) What action should be taken?
d) Why did it happen?
3. What do prescriptive questions ask?
a) What came about?
b) What will take place?
c) What action(s) might be best? (e.g., where to position traffic lights)
d) When did it happen?
4. What is a “version” (model) in analytics?
a) A software program
b) A real-life scenario expressed as math
c) A type of data
d) A prediction algorithm
5. What do classifiers help you do?
a) Predict continuous values
b) Differentiate
c) Cluster similar items
d) Reduce dimensions
,6. What is a soft classifier and when is it used?
a) A classifier that always separates data perfectly
b) A classifier that uses only linear boundaries
c) In cases where there is no line that separates all classified examples – we
minimize the number of mistakes
d) A classifier that never makes errors
7. What does it mean when the classifier/decision boundary is almost parallel
to the vertical x-axis?
a) The vertical feature is all that is needed
b) The horizontal feature is all that is needed
c) Both features are equally important
d) The classifier is overfit
8. What does it mean when the classifier/decision boundary is almost parallel
to the horizontal y-axis?
a) The horizontal feature is all that is needed
b) The vertical feature is all that is needed
c) Neither feature matters
d) The classifier is underfit
9. What is time-series data?
a) Data recorded at random intervals
b) The same data recorded over time, often at equal periods
c) Data that is not time-dependent
d) Cross-sectional data
10. What is quantitative data?
a) Numbers with meaning: higher means more, lower means less (e.g., age,
income, temperature)
b) Numbers without meaning (e.g., zip codes)
c) Non-numeric data
d) Binary data only
11. What is categorical data?
a) Numbers with meaning (e.g., temperature)
b) Numbers without meaning (e.g., zip codes), non-numeric (e.g., hair color),
or binary (e.g., male/female)
c) Continuous measurements
d) Time-stamped data
, 12. Which of these is time series data?
a) The average price of a house in the United States every year since 1820
b) The height of each professional basketball player in the NBA at the start of the
season
c) The brand of car each person drives
d) The zip code of survey respondents
13. Which of these is structured data?
a) The contents of someone’s Twitter feed
b) The amount of money in a person’s bank account
c) A video recording
d) An email body
14. What is structured data?
a) Data that is messy and unorganized
b) Data that can be stored in a structured manner (e.g., rows and columns)
c) Data that only contains images
d) Data that cannot be described easily
15. What is unstructured data?
a) Data stored in a relational database
b) Data that is not easily described and stored (e.g., written text, images,
video)
c) Numerical data only
d) Data with a fixed schema
16. A survey of 25 people recorded each person’s family size and type of car.
Which of these is a data point?
a) The 14th person’s family size and car type
b) The 14th person’s family size only
c) The car type of everyone
d) The average family size
17. The farther the wrongly classified point is from the line …
a) The smaller the error
b) The bigger the error we have made
c) The more confident the classifier is
d) The less it matters
