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ISYE6501 / ISYE 6501 Midterm Exam 1 (Latest Update
) Intro to Analytics Modeling | Questions &
Answers | Grade A | 100% Correct Georgia Tech
1. A nursing instructor is explaining the concept of outlier detection to students. Which
statement best describes a contextual outlier in a dataset?
A. A value that is significantly higher than the global average of all data points.
B. A value that is not extreme compared to the whole dataset but is unusual for a
specific point in time or location.
C. A value that falls exactly on the median of the dataset.
D. A value that is identified only through imputation methods.
CORRECT ANSWER: B
Rationale: A contextual outlier, also known as a conditional outlier, is a data point that
deviates significantly from other data points in the same context (e.g., time or location),
even if it is not an outlier in the overall dataset.
2. A nurse researcher is comparing supervised and unsupervised learning methods.
What is the fundamental difference between these two approaches?
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A. Supervised learning is used for prediction, while unsupervised learning is used for
prescription.
B. Supervised learning requires a larger sample size than unsupervised learning.
C. In supervised learning, the outcome or response variable is known and used to guide
the model creation; in unsupervised learning, the response is not known.
D. Unsupervised learning uses structured data, while supervised learning uses
unstructured data.
CORRECT ANSWER: C
Rationale: The key distinction is that supervised learning algorithms are trained on
labeled data, where the correct answer (response) is provided. Unsupervised learning
algorithms identify patterns and structures in data without any pre-existing labels or
known outcomes.
3. A nurse is analyzing patient data and identifies a data point that is extremely different
from the rest of the observations. What is the correct term for this data point?
A. A confounder
B. A variance
C. An outlier
D. A bias
CORRECT ANSWER: C
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Rationale: An outlier is an observation point that is distant from other observations in a
dataset. It can be caused by variability in the measurement or may indicate
experimental error.
4. A charge nurse is evaluating the performance of a new screening tool. What key piece
of information does the ROC/AUC provide, and what important limitation does it have?
A. It gives a precise measure of model accuracy but does not account for sample size.
B. It gives a quick estimate of a model's overall ability to discriminate but does not
differentiate between the costs of false positives and false negatives.
C. It differentiates between the costs of different error types but is difficult to calculate.
D. It provides the exact probability of a correct prediction for any given threshold.
CORRECT ANSWER: B
Rationale: The Receiver Operating Characteristic (ROC) curve and its Area Under the
Curve (AUC) provide a summary measure of a model's performance across all
classification thresholds. However, it treats false positives and false negatives equally,
which is a limitation when the consequences of these errors are different.
5. A nurse is reading a research article that reports the sensitivity of a new diagnostic
test. How is sensitivity defined?
A. The fraction of non-category members that are correctly identified (TN / (TN + FP)).
B. The fraction of category members that are correctly classified (TP / (TP + FN)).
ISYE6501 / ISYE 6501 Midterm Exam 1 (Latest Update
) Intro to Analytics Modeling | Questions &
Answers | Grade A | 100% Correct Georgia Tech
1. A nursing instructor is explaining the concept of outlier detection to students. Which
statement best describes a contextual outlier in a dataset?
A. A value that is significantly higher than the global average of all data points.
B. A value that is not extreme compared to the whole dataset but is unusual for a
specific point in time or location.
C. A value that falls exactly on the median of the dataset.
D. A value that is identified only through imputation methods.
CORRECT ANSWER: B
Rationale: A contextual outlier, also known as a conditional outlier, is a data point that
deviates significantly from other data points in the same context (e.g., time or location),
even if it is not an outlier in the overall dataset.
2. A nurse researcher is comparing supervised and unsupervised learning methods.
What is the fundamental difference between these two approaches?
,2|Page
A. Supervised learning is used for prediction, while unsupervised learning is used for
prescription.
B. Supervised learning requires a larger sample size than unsupervised learning.
C. In supervised learning, the outcome or response variable is known and used to guide
the model creation; in unsupervised learning, the response is not known.
D. Unsupervised learning uses structured data, while supervised learning uses
unstructured data.
CORRECT ANSWER: C
Rationale: The key distinction is that supervised learning algorithms are trained on
labeled data, where the correct answer (response) is provided. Unsupervised learning
algorithms identify patterns and structures in data without any pre-existing labels or
known outcomes.
3. A nurse is analyzing patient data and identifies a data point that is extremely different
from the rest of the observations. What is the correct term for this data point?
A. A confounder
B. A variance
C. An outlier
D. A bias
CORRECT ANSWER: C
, 3|Page
Rationale: An outlier is an observation point that is distant from other observations in a
dataset. It can be caused by variability in the measurement or may indicate
experimental error.
4. A charge nurse is evaluating the performance of a new screening tool. What key piece
of information does the ROC/AUC provide, and what important limitation does it have?
A. It gives a precise measure of model accuracy but does not account for sample size.
B. It gives a quick estimate of a model's overall ability to discriminate but does not
differentiate between the costs of false positives and false negatives.
C. It differentiates between the costs of different error types but is difficult to calculate.
D. It provides the exact probability of a correct prediction for any given threshold.
CORRECT ANSWER: B
Rationale: The Receiver Operating Characteristic (ROC) curve and its Area Under the
Curve (AUC) provide a summary measure of a model's performance across all
classification thresholds. However, it treats false positives and false negatives equally,
which is a limitation when the consequences of these errors are different.
5. A nurse is reading a research article that reports the sensitivity of a new diagnostic
test. How is sensitivity defined?
A. The fraction of non-category members that are correctly identified (TN / (TN + FP)).
B. The fraction of category members that are correctly classified (TP / (TP + FN)).
