ISYE 6501 FINAL PAPER 2026 QUESTIONS AND
SOLUTIONS GRADED A+
◉ 2-norm. Answer: Similar to Euclidian distance; measures the
straight-line length of a vector from the origin. If 𝑧𝑧 = (𝑧𝑧1, 𝑧𝑧2, ... ,
𝑧𝑧𝑚𝑚) is a vector in an 𝑚𝑚- dimensional space, then its 2-norm is
�(𝑧𝑧1)2 + (𝑧𝑧2)2 + ⋯ + (𝑧𝑧𝑚𝑚)2 2 = �∑ (𝑧𝑧𝑖𝑖) 𝑚𝑚 2 𝑖𝑖=1 2 .
◉ A/B testing. Answer: Test of two alternatives to see if either one
leads to better outcomes.
◉ Accuracy. Answer: Fraction of data points correctly classified by a
model; equal to 𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹.
◉ Action. Answer: In ARENA, something that is done to an entity.
◉ 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/Adjusted R2. Answer: Variant of R2 that
encourages simpler models by penalizing the use of too many
variables
,◉ AIC. Answer: Akaike information criterion
◉ Akaike information criterion (AIC). Answer: 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.
◉ 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 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.
◉ 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: 𝑃𝑃(𝐴𝐴|𝐵𝐵) = 𝑃𝑃(𝐵𝐵|𝐴𝐴)𝑃𝑃(𝐴𝐴) 𝑃𝑃(𝐵𝐵) .
◉ Bayesian Information criterion (BIC). Answer: Model selection
technique that trades off model fit and model complexity. When
comparing models, the model with lower BIC is preferred. Generally
penalizes complexity more than AIC.
◉ Bayesian regression. Answer: Regression model that incorporates
estimates of how coefficients and error are distributed.
◉ Bellman's equation. Answer: Equation used in dynamic
programming that ensures optimality of a solution.
◉ Bernoulli distribution. Answer: Discrete probability distribution
where the outcome is binary, either 0 or 1. Often, 1 represents
success and 0 represents failure. The probability of the outcome
SOLUTIONS GRADED A+
◉ 2-norm. Answer: Similar to Euclidian distance; measures the
straight-line length of a vector from the origin. If 𝑧𝑧 = (𝑧𝑧1, 𝑧𝑧2, ... ,
𝑧𝑧𝑚𝑚) is a vector in an 𝑚𝑚- dimensional space, then its 2-norm is
�(𝑧𝑧1)2 + (𝑧𝑧2)2 + ⋯ + (𝑧𝑧𝑚𝑚)2 2 = �∑ (𝑧𝑧𝑖𝑖) 𝑚𝑚 2 𝑖𝑖=1 2 .
◉ A/B testing. Answer: Test of two alternatives to see if either one
leads to better outcomes.
◉ Accuracy. Answer: Fraction of data points correctly classified by a
model; equal to 𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹.
◉ Action. Answer: In ARENA, something that is done to an entity.
◉ 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/Adjusted R2. Answer: Variant of R2 that
encourages simpler models by penalizing the use of too many
variables
,◉ AIC. Answer: Akaike information criterion
◉ Akaike information criterion (AIC). Answer: 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.
◉ 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 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.
◉ 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: 𝑃𝑃(𝐴𝐴|𝐵𝐵) = 𝑃𝑃(𝐵𝐵|𝐴𝐴)𝑃𝑃(𝐴𝐴) 𝑃𝑃(𝐵𝐵) .
◉ Bayesian Information criterion (BIC). Answer: Model selection
technique that trades off model fit and model complexity. When
comparing models, the model with lower BIC is preferred. Generally
penalizes complexity more than AIC.
◉ Bayesian regression. Answer: Regression model that incorporates
estimates of how coefficients and error are distributed.
◉ Bellman's equation. Answer: Equation used in dynamic
programming that ensures optimality of a solution.
◉ Bernoulli distribution. Answer: Discrete probability distribution
where the outcome is binary, either 0 or 1. Often, 1 represents
success and 0 represents failure. The probability of the outcome
