ISYE 6501 FINAL EVALUATION QUIZ
COMPLETE QUESTIONS AND RESPONSES
2026 PASS CONFIRMED.
⫸ A/B Testing. Answer: testing two alternatives to see which one
performs better
⫸ 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)
⫸ 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.
⫸ 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.
⫸ 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.
⫸ 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)
⫸ 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 being 1 is 𝑝
and the probability of outcome being 0 is 𝑞 = 1−𝑝, where 𝑝 is between
0 and 1.
⫸ Bias. Answer: Systematic difference between a true parameter of a
population and its estimate.
COMPLETE QUESTIONS AND RESPONSES
2026 PASS CONFIRMED.
⫸ A/B Testing. Answer: testing two alternatives to see which one
performs better
⫸ 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)
⫸ 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.
⫸ 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.
⫸ 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.
⫸ 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)
⫸ 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 being 1 is 𝑝
and the probability of outcome being 0 is 𝑞 = 1−𝑝, where 𝑝 is between
0 and 1.
⫸ Bias. Answer: Systematic difference between a true parameter of a
population and its estimate.
