ISYE 6501 Final Quizzes (V1, V2 & V3):
Questions and Answers – 100% Correct,
Updated 2026
True negative (TN) - ANSWER Data point that a model correctly classifies as not
being in a certain category. ("Negative" means the model classified it as not being
in the category, and "True" means the model's classification is correct.) Sometimes
abbreviated as "TN"
True negative rate - ANSWER Fraction of data points not in a certain category
that are correctly classified by a model; equal to 𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹 ; also called
specificity
True positive (TP) - ANSWER Data point that a model correctly classifies as
being in a certain category. ("Positive" means the model classified it as being in the
category, and "True" means the model's classification is correct.) Sometimes
abbreviated as "TP"
True positive rate - ANSWER Fraction of data points in a certain category that are
correctly classified by a model; equal to 𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹; also called
sensitivity, hit rate, and recall
Uncertainty - ANSWER Lack of knowledge about a data value, parameter value,
outcome, etc.
Unstructured data - ANSWER Data that is not very well organized for analysis -
for example, a list of free responses to the question "What do you like about
analytics?"
Unsupervised learning - ANSWER Machine learning where the "correct" answer
is not known for the data points in the training set.
Upper tail - ANSWER Highest-value part of a distribution
Validation - ANSWER Measuring a model's effectiveness on data that was not
used to build/train/fit the model. If there is a large difference between a model's
,effectiveness on a validation set of data and its effectiveness on the training set of
data, it is evidence that the model may be overfit.
Validation (of simulation) - ANSWER Making sure that simulation results are
similar-enough to those of the real system being simulated, so the simulation can
be used to analyze the real system.
Validation data/validation set - ANSWER Portion of the data used for validation
of a model and compare between models.
Variable (optimization sense) - ANSWER A decision that an optimization model
suggests a value for.
Variable (statistics sense) - ANSWER An attribute whose value can differ for
different data points.
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.
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 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
Questions and Answers – 100% Correct,
Updated 2026
True negative (TN) - ANSWER Data point that a model correctly classifies as not
being in a certain category. ("Negative" means the model classified it as not being
in the category, and "True" means the model's classification is correct.) Sometimes
abbreviated as "TN"
True negative rate - ANSWER Fraction of data points not in a certain category
that are correctly classified by a model; equal to 𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹 ; also called
specificity
True positive (TP) - ANSWER Data point that a model correctly classifies as
being in a certain category. ("Positive" means the model classified it as being in the
category, and "True" means the model's classification is correct.) Sometimes
abbreviated as "TP"
True positive rate - ANSWER Fraction of data points in a certain category that are
correctly classified by a model; equal to 𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹; also called
sensitivity, hit rate, and recall
Uncertainty - ANSWER Lack of knowledge about a data value, parameter value,
outcome, etc.
Unstructured data - ANSWER Data that is not very well organized for analysis -
for example, a list of free responses to the question "What do you like about
analytics?"
Unsupervised learning - ANSWER Machine learning where the "correct" answer
is not known for the data points in the training set.
Upper tail - ANSWER Highest-value part of a distribution
Validation - ANSWER Measuring a model's effectiveness on data that was not
used to build/train/fit the model. If there is a large difference between a model's
,effectiveness on a validation set of data and its effectiveness on the training set of
data, it is evidence that the model may be overfit.
Validation (of simulation) - ANSWER Making sure that simulation results are
similar-enough to those of the real system being simulated, so the simulation can
be used to analyze the real system.
Validation data/validation set - ANSWER Portion of the data used for validation
of a model and compare between models.
Variable (optimization sense) - ANSWER A decision that an optimization model
suggests a value for.
Variable (statistics sense) - ANSWER An attribute whose value can differ for
different data points.
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.
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 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
