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ISYE 6501 Final Exam Questions and Answers 2025/2026 | Georgia Tech | Verified Study Guide

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Prepare for the ISYE 6501 Final Exam with this comprehensive 2025/2026 study guide. Includes verified exam questions and answers covering key topics like optimization, simulation, machine learning, and statistical modeling. Perfect for Georgia Tech students aiming for top scores. Download now and ace your exam!

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ISYE 6501 Final Exam | Georgia Tech | 2025/2026
Edition | Verified Exam Questions with Answers



1-norm - ...ANSWER✓✓✓Similar to rectilinear distance;
measures the straight-line length of a vector from the origin. If
z=(z1,z2,...,zm) is a vector in an m-dimensional space, then it's 1-
norm is square root(|𝑧1|+|𝑧2|+⋯+|𝑧𝑚| = |𝑧1|+|𝑧2|+⋯+|𝑧| = Σm over
i=1 |𝑧𝑖|


2-norm - ...ANSWER✓✓✓Similar to Euclidian distance; measures
the straight-line length of a vector from the origin. If
z=(z1,z2,...,zm) is a vector in an 𝑚-dimensional space, then its 2-
norm is the same as 1-norm but everything is squared= square
root(Σm over i=1 (|𝑧𝑖|)^2)


A/B Testing - ...ANSWER✓✓✓testing two alternatives to see
which one performs better


Accuracy - ...ANSWER✓✓✓Fraction of data points correctly
classified by a model; equal to TP+TN / TP+FP+TN+FN


Action - ...ANSWER✓✓✓In ARENA, something that is done to an
entity.

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Additive Seasonality - ...ANSWER✓✓✓Seasonal effect that is
added to a baseline value (for example, "the temperature in June
is 10 degrees above the annual baseline").


Adjusted R-squared - ...ANSWER✓✓✓Variant of R2 that
encourages simpler models by penalizing the use of too many
variables.


AIC - ...ANSWER✓✓✓Akaike information criterion- Model
selection technique that trades off between model fit and model
complexity. When comparing models, the model with lower AIC is
preferred. Generally penalizes complexity less than BIC.


Algorithm - ...ANSWER✓✓✓Step-by-step procedure designed to
carry out a task.


Analysis of Variance/ANOVA - ...ANSWER✓✓✓Statistical method
for dividing the variation in observations among different
sources.


Approximate dynamic program - ...ANSWER✓✓✓Dynamic
programming model where the value functions are
approximated.

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Arc - ...ANSWER✓✓✓Connection between two nodes/vertices in
a network. In a network model, there is a variable for each arc,
equal to the amount of flow on the arc, and (optionally) a capacity
constraint on the arc's flow. Also called an edge.


Area under the curve (AUC) - ...ANSWER✓✓✓Area under the
ROC curve; an estimate of the classification model's accuracy.
Also called concordance index.


ARIMA - ...ANSWER✓✓✓Autoregressive integrated moving
average.


Arrival Rate - ...ANSWER✓✓✓Expected number of arrivals of
people, things, etc. per unit time -- for example, the expected
number of truck deliveries per hour to a warehouse.


Assignment Problem - ...ANSWER✓✓✓Network optimization
model with two sets of nodes, that finds the best way to assign
each node in one set to each node in the other set.


Attribute - ...ANSWER✓✓✓A characteristic or measurement - for
example, a person's height or the color of a car. Generally
interchangeable with "feature", and often with "covariate" or
"predictor". In the standard tabular format, a column of data.

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Autoregression - ...ANSWER✓✓✓Regression technique using
past values of time series data as predictors of future values.


Autoregressive integrated moving average (ARIMA) -
...ANSWER✓✓✓Time series model that uses differences between
observations when data is nonstationary. Also called Box-
Jenkins.


Backward elimination - ...ANSWER✓✓✓Variable selection
process that starts with all variables and then iteratively
removes the least-immediately-relevant variables from the
model.


Balanced Design - ...ANSWER✓✓✓Set of combinations of factor
values across multiple factors, that has the same number of
runs for all combinations of levels of one or more factors.


Balking - ...ANSWER✓✓✓An entity arrives to the queue, sees the
size of the line (or some other attribute), and decides to leave the
system.


Bayes' theorem/Bayes' rule - ...ANSWER✓✓✓Fundamental rule
of conditional probability: 𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)

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