ISYE 6501 Final Quiz | Questions and Answers |
2026 Update | 100% Correct.
Q: What do descriptive questions ask?
Answer: What happened? (e.g., which customers are most alike)
Rationale: Descriptive analytics focuses on summarizing historical
data to identify patterns, trends, and relationships. It answers "what
happened?" rather than predicting future outcomes or prescribing
actions.
Q: What do predictive questions ask?
Answer: What will happen? (e.g., what will Google's stock price be?)
Rationale: Predictive analytics uses historical data and statistical
models to forecast future events or values. It answers "what will
happen?" based on patterns identified in past data.
Q: What do prescriptive questions ask?
Answer: What action(s) would be best? (e.g., where to put traffic
lights)
Rationale: Prescriptive analytics recommends specific actions or
decisions to achieve desired outcomes. It answers "what should we
do?" by evaluating multiple scenarios and optimizing choices.
,Q: What is a model?
Answer: Real-life situation expressed as math.
Rationale: A model is an abstraction or simplification of reality that
captures key relationships using mathematical equations, algorithms,
or logical rules to analyze, predict, or optimize outcomes.
Q: What do classifiers help you do?
Answer: Differentiate
Rationale: Classification models assign data points to predefined
categories or classes. They learn decision boundaries that separate
different groups based on input features.
Q: What is a soft classifier and when is it used?
Answer: In some cases, there won't be a line that separates all of the
labeled examples. So we use a classifier that minimizes the number of
mistakes.
Rationale: Soft classifiers allow for misclassifications and trade off
between accuracy and margin width. They are essential when data is
not perfectly separable, using regularization parameters to balance
error minimization and generalization.
,Q: What does it mean when the classifier/decision boundary is
almost parallel to the vertical x-axis?
Answer: The horizontal attribute is all that is needed.
Rationale: When a decision boundary is nearly vertical, changes in the
x-axis variable have little effect on classification. This indicates the y-
axis variable dominates the separation between classes.
Q: What does it mean when the classifier/decision boundary is
almost parallel to the horizontal y-axis?
Answer: The vertical attribute is all that is needed.
Rationale: When a decision boundary is nearly horizontal, changes in
the y-axis variable have little effect on classification. This indicates the
x-axis variable dominates the separation between classes.
Q: What is time-series data?
Answer: The same data recorded over time, often recorded at equal
intervals.
Rationale: Time-series data consists of sequential observations
ordered chronologically. Key characteristics include trend, seasonality,
autocorrelation, and potential non-stationarity.
Q: What is quantitative data?
, Answer: Number with a meaning: higher means more, lower means
less (e.g., age, sales, temperature, income).
Rationale: Quantitative data represents measurable quantities where
mathematical operations are meaningful. It can be discrete (counts) or
continuous (measurements).
Q: What is categorical data?
Answer: Numbers without meaning (e.g., zip codes), non-numeric (e.g.,
hair color), binary data (e.g., male/female, yes/no, on/off).
Rationale: Categorical data represents groups or categories where
numerical values, if assigned, are arbitrary labels. Mathematical
operations between categories are not meaningful.
Q: Which of these is time series data?
A. The average cost 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
Answer: A
Rationale: Time-series data requires multiple observations of the
same variable over time. Option A tracks housing costs annually for
200+ years. Option B is cross-sectional data—one observation per
player at a single time point.
2026 Update | 100% Correct.
Q: What do descriptive questions ask?
Answer: What happened? (e.g., which customers are most alike)
Rationale: Descriptive analytics focuses on summarizing historical
data to identify patterns, trends, and relationships. It answers "what
happened?" rather than predicting future outcomes or prescribing
actions.
Q: What do predictive questions ask?
Answer: What will happen? (e.g., what will Google's stock price be?)
Rationale: Predictive analytics uses historical data and statistical
models to forecast future events or values. It answers "what will
happen?" based on patterns identified in past data.
Q: What do prescriptive questions ask?
Answer: What action(s) would be best? (e.g., where to put traffic
lights)
Rationale: Prescriptive analytics recommends specific actions or
decisions to achieve desired outcomes. It answers "what should we
do?" by evaluating multiple scenarios and optimizing choices.
,Q: What is a model?
Answer: Real-life situation expressed as math.
Rationale: A model is an abstraction or simplification of reality that
captures key relationships using mathematical equations, algorithms,
or logical rules to analyze, predict, or optimize outcomes.
Q: What do classifiers help you do?
Answer: Differentiate
Rationale: Classification models assign data points to predefined
categories or classes. They learn decision boundaries that separate
different groups based on input features.
Q: What is a soft classifier and when is it used?
Answer: In some cases, there won't be a line that separates all of the
labeled examples. So we use a classifier that minimizes the number of
mistakes.
Rationale: Soft classifiers allow for misclassifications and trade off
between accuracy and margin width. They are essential when data is
not perfectly separable, using regularization parameters to balance
error minimization and generalization.
,Q: What does it mean when the classifier/decision boundary is
almost parallel to the vertical x-axis?
Answer: The horizontal attribute is all that is needed.
Rationale: When a decision boundary is nearly vertical, changes in the
x-axis variable have little effect on classification. This indicates the y-
axis variable dominates the separation between classes.
Q: What does it mean when the classifier/decision boundary is
almost parallel to the horizontal y-axis?
Answer: The vertical attribute is all that is needed.
Rationale: When a decision boundary is nearly horizontal, changes in
the y-axis variable have little effect on classification. This indicates the
x-axis variable dominates the separation between classes.
Q: What is time-series data?
Answer: The same data recorded over time, often recorded at equal
intervals.
Rationale: Time-series data consists of sequential observations
ordered chronologically. Key characteristics include trend, seasonality,
autocorrelation, and potential non-stationarity.
Q: What is quantitative data?
, Answer: Number with a meaning: higher means more, lower means
less (e.g., age, sales, temperature, income).
Rationale: Quantitative data represents measurable quantities where
mathematical operations are meaningful. It can be discrete (counts) or
continuous (measurements).
Q: What is categorical data?
Answer: Numbers without meaning (e.g., zip codes), non-numeric (e.g.,
hair color), binary data (e.g., male/female, yes/no, on/off).
Rationale: Categorical data represents groups or categories where
numerical values, if assigned, are arbitrary labels. Mathematical
operations between categories are not meaningful.
Q: Which of these is time series data?
A. The average cost 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
Answer: A
Rationale: Time-series data requires multiple observations of the
same variable over time. Option A tracks housing costs annually for
200+ years. Option B is cross-sectional data—one observation per
player at a single time point.
