ISyE 6501 FInal Exam QuIz | QuEStIonS and CorrECt
anSwErS (VErIFIEd anSwErS) alrEady GradEd a+ | 2026
updatE|| paSS!
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.
1-norm - anSwEr-Similar to rectilinear distance; measures the sum of the lengths of each dimension of
a vector from the origin. If 𝑧𝑧 = (𝑧𝑧1, 𝑧𝑧2, ... , 𝑧𝑧𝑚𝑚) is a vector in an 𝑚𝑚-dimensional space, then its 1-
norm is �|𝑧𝑧1|1 + |𝑧𝑧2|1 + ⋯ + |𝑧𝑧𝑚𝑚| 1 1 = |𝑧𝑧1| + |𝑧𝑧2| + ⋯ + |𝑧𝑧| = ∑ |𝑧𝑧𝑖𝑖| 𝑚𝑚 𝑖𝑖=1 .
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.
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 being 1 is 𝑝𝑝
and the probability of outcome being 0 is 𝑞𝑞 = 1 − 𝑝𝑝, where 𝑝𝑝 is between 0 and 1.
Bias - anSwEr-Systematic difference between a true parameter of a population and its estimate
BIC - anSwEr-Bayesian information criterion
, Binary data - anSwEr-Data that can take only two different values (true/false, 0/1, black/white, on/off,
etc.).
Binary integer program - anSwEr-Integer program where all variables are binary variables.
Binary variable - anSwEr-Variable that can take just two values: 0 and 1.
Binomial distribution - anSwEr-Discrete probability distribution for the exact number of successes, k,
out of a total of n iid Bernoulli trials, each with probability p: Pr(𝑘𝑘) = � 𝑛𝑛 𝑘𝑘� 𝑝𝑝𝑘𝑘(1 − 𝑝𝑝)𝑛𝑛−𝑘𝑘.
Blocking - anSwEr-Factor introduced to an experimental design that interacts with the effect of the
factors to be studied. The effect of the factors is studied within the same level (block) of the blocking
factor
Box and whisker plot - anSwEr-Graphical representation data showing the middle range of data (the
"box"), reasonable ranges of variability ("whiskers"), and points (possible outliers) outside those ranges.
Box-Cox transformation - anSwEr-Transformation of a non-normally-distributed response to a normal
distribution.
Branching - anSwEr-Splitting a set of data into two or more subsets, to each be analyzed separately.
CART - anSwEr-Classification and regression trees.
Change detection - anSwEr-Identifying when a significant change has taken place in a process.
Classification - anSwEr-The separation of data into two or more categories, or (a point's classification)
the category a data point is put into.
Classification tree - anSwEr-Tree-based method for classification. After branching to split the data,
each subset is analyzed with its own classification model.
anSwErS (VErIFIEd anSwErS) alrEady GradEd a+ | 2026
updatE|| paSS!
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.
1-norm - anSwEr-Similar to rectilinear distance; measures the sum of the lengths of each dimension of
a vector from the origin. If 𝑧𝑧 = (𝑧𝑧1, 𝑧𝑧2, ... , 𝑧𝑧𝑚𝑚) is a vector in an 𝑚𝑚-dimensional space, then its 1-
norm is �|𝑧𝑧1|1 + |𝑧𝑧2|1 + ⋯ + |𝑧𝑧𝑚𝑚| 1 1 = |𝑧𝑧1| + |𝑧𝑧2| + ⋯ + |𝑧𝑧| = ∑ |𝑧𝑧𝑖𝑖| 𝑚𝑚 𝑖𝑖=1 .
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.
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 being 1 is 𝑝𝑝
and the probability of outcome being 0 is 𝑞𝑞 = 1 − 𝑝𝑝, where 𝑝𝑝 is between 0 and 1.
Bias - anSwEr-Systematic difference between a true parameter of a population and its estimate
BIC - anSwEr-Bayesian information criterion
, Binary data - anSwEr-Data that can take only two different values (true/false, 0/1, black/white, on/off,
etc.).
Binary integer program - anSwEr-Integer program where all variables are binary variables.
Binary variable - anSwEr-Variable that can take just two values: 0 and 1.
Binomial distribution - anSwEr-Discrete probability distribution for the exact number of successes, k,
out of a total of n iid Bernoulli trials, each with probability p: Pr(𝑘𝑘) = � 𝑛𝑛 𝑘𝑘� 𝑝𝑝𝑘𝑘(1 − 𝑝𝑝)𝑛𝑛−𝑘𝑘.
Blocking - anSwEr-Factor introduced to an experimental design that interacts with the effect of the
factors to be studied. The effect of the factors is studied within the same level (block) of the blocking
factor
Box and whisker plot - anSwEr-Graphical representation data showing the middle range of data (the
"box"), reasonable ranges of variability ("whiskers"), and points (possible outliers) outside those ranges.
Box-Cox transformation - anSwEr-Transformation of a non-normally-distributed response to a normal
distribution.
Branching - anSwEr-Splitting a set of data into two or more subsets, to each be analyzed separately.
CART - anSwEr-Classification and regression trees.
Change detection - anSwEr-Identifying when a significant change has taken place in a process.
Classification - anSwEr-The separation of data into two or more categories, or (a point's classification)
the category a data point is put into.
Classification tree - anSwEr-Tree-based method for classification. After branching to split the data,
each subset is analyzed with its own classification model.
