ISYE MIDTERM 2 | FALL | PRACTICE
EXAMINATION 2026 QUESTIONS WITH
ANSWERS GRADED A+
◍ Logit model.
Answer: Regression model that uses an exponential function of variables to
estimate a response between 0 and 1: y= + ^-(a0+Σ m over i=1
). Also called a logistic regression.
◍ Accuracy.
Answer: Fraction of data points correctly classified by a model; equal to
TP+TN / TP+FP+TN+FN
◍ Optimization.
Answer: Finding the values of variables/decisions that yield the best value of
an objective function while satisfying a set of constraints (restrictions).
◍ Weibull Distribution.
Answer: Continuous probability distribution that is often used to model the
time until failure of a device, component, etc.: ( )= / ( / )^ −1
^−( / )^ for ≥0.
◍ Machine Learning.
Answer: Use of computer algorithms to learn and discover patterns or
structure in data, without being programmed specifically for them.
◍ Interaction term.
Answer: Variable in a model that is the combination of two or more other
variables; for example, if 1 and 2 are variables, ( 1 2) is an interaction
term/interaction variable.
◍ Causation.
, Answer: Relationship in which one thing makes another happen (i.e., one
thing causes another).
◍ R-squared.
Answer: Measure of linear regression model quality, the fraction of variance
in the response that is explained by the model. Also called coefficient of
determination.
◍ Network Optimization problem.
Answer: Optimization problem that can be modeled as a network with nodes
and arcs, where each variable represents the flow on an arc, with constraints
to ensure that the flow into each node equals the flow out of it, and to put a
capacity on the flow on each arc.
◍ Stochastic dynamic programming.
Answer: Dynamic program where the outcome of one or more decisions is
determined according to probabilities.
◍ Global optimum/maximum/minimum.
Answer: A solution that achieves the best objective value among all of the
feasible solutions; sometimes also used to refer to the best objective value
achievable among a set of feasible solutions.
◍ Double exponential smoothing.
Answer: Two-parameter exponential smoothing technique that incorporates
trend.
◍ Prediction.
Answer: Estimate of what will happen in the future, or of something
unknown (e.g., missing data) that happened.
◍ Minimization Problem.
Answer: Optimization model where the objective is to find the feasible
solution that minimizes the value of the objective function.
◍ Memoryless (distribution).
Answer: Probability distributions where the past history of outcomes does
, not influence the probability of the outcome of future events. The
exponential and geometric distributions have this property.
◍ linear inequality.
Answer: Inequality where a linear function is set to be
greater-than-or-equal-to or less-than-or-equal-to a constant or another linear
function.
◍ Convex function.
Answer: A function f() where for every two points and , ( x+
(1− ) ) ≤ f( ) + (1− ) ( ) for all between 0 and 1. In two
dimensions, this means if the points ( , ( )) and ( , ( )) are connected
with a straight line, the line is always above [or equal to] the function's
curve between those two points. If () is convex, then − () is concave.
◍ EM Algorithm.
Answer: Expectation-maximization algorithm.
◍ Pseudo-R-squared/Pseudo-R^2.
Answer: Measure similar to R^2 used for nonlinear regression models where
R^2 cannot be calculated.
◍ Autoregression.
Answer: Regression technique using past values of time series data as
predictors of future values.
◍ Binary Data.
Answer: Data that can take only two different values (true/false, 0/1,
black/white, on/off, etc.)
◍ Robust Solution.
Answer: A solution that whose worst-case outcome over all possible
scenarios is least bad.
◍ Convex Set.
Answer: A set of points for which a straight line drawn between any two
points in the set, stays inside the set. A circle is a convex set. A set shaped
, like the letter "U" is not convex; the line between the two points on top goes
outside of the set.
◍ False Negative (FN).
Answer: Data point that a model incorrectly classifies as not being in a
certain category. ("Negative" means the model classified it as not being in
the category, and "False" means the model's classification is incorrect.)
Sometimes abbreviated as "FN".
◍ Seasonality length/cycle length.
Answer: Fixed time period at which cycles/seasonalities repeat themselves.
◍ Correlation.
Answer: Relationship in which two things are likely to happen together,
regardless of whether one causes the other. (There is also a quantitative
statistical definition measuring the amount of correlation.)
◍ Missing data.
Answer: Values of data that are missing from a data set
◍ interarrival times.
Answer: The time between two consecutive arrivals of people, things, etc. --
for example, the time between consecutive phone calls to a service hotline.
◍ Greedy Algorithm.
Answer: Algorithm that makes the immediately-best choice at each step.
◍ Change Detection.
Answer: Identifying when a significant change has taken place in a process.
◍ Overfitting.
Answer: Building a model that describes random effects instead of or in
significant addition to the real effects; often caused by having too many
factors or parameters compared to the number of data points. Will have high
prediction errors.
◍ Decision Point.
Answer: Place in a simulation where there is a branch (or decision to be
EXAMINATION 2026 QUESTIONS WITH
ANSWERS GRADED A+
◍ Logit model.
Answer: Regression model that uses an exponential function of variables to
estimate a response between 0 and 1: y= + ^-(a0+Σ m over i=1
). Also called a logistic regression.
◍ Accuracy.
Answer: Fraction of data points correctly classified by a model; equal to
TP+TN / TP+FP+TN+FN
◍ Optimization.
Answer: Finding the values of variables/decisions that yield the best value of
an objective function while satisfying a set of constraints (restrictions).
◍ Weibull Distribution.
Answer: Continuous probability distribution that is often used to model the
time until failure of a device, component, etc.: ( )= / ( / )^ −1
^−( / )^ for ≥0.
◍ Machine Learning.
Answer: Use of computer algorithms to learn and discover patterns or
structure in data, without being programmed specifically for them.
◍ Interaction term.
Answer: Variable in a model that is the combination of two or more other
variables; for example, if 1 and 2 are variables, ( 1 2) is an interaction
term/interaction variable.
◍ Causation.
, Answer: Relationship in which one thing makes another happen (i.e., one
thing causes another).
◍ R-squared.
Answer: Measure of linear regression model quality, the fraction of variance
in the response that is explained by the model. Also called coefficient of
determination.
◍ Network Optimization problem.
Answer: Optimization problem that can be modeled as a network with nodes
and arcs, where each variable represents the flow on an arc, with constraints
to ensure that the flow into each node equals the flow out of it, and to put a
capacity on the flow on each arc.
◍ Stochastic dynamic programming.
Answer: Dynamic program where the outcome of one or more decisions is
determined according to probabilities.
◍ Global optimum/maximum/minimum.
Answer: A solution that achieves the best objective value among all of the
feasible solutions; sometimes also used to refer to the best objective value
achievable among a set of feasible solutions.
◍ Double exponential smoothing.
Answer: Two-parameter exponential smoothing technique that incorporates
trend.
◍ Prediction.
Answer: Estimate of what will happen in the future, or of something
unknown (e.g., missing data) that happened.
◍ Minimization Problem.
Answer: Optimization model where the objective is to find the feasible
solution that minimizes the value of the objective function.
◍ Memoryless (distribution).
Answer: Probability distributions where the past history of outcomes does
, not influence the probability of the outcome of future events. The
exponential and geometric distributions have this property.
◍ linear inequality.
Answer: Inequality where a linear function is set to be
greater-than-or-equal-to or less-than-or-equal-to a constant or another linear
function.
◍ Convex function.
Answer: A function f() where for every two points and , ( x+
(1− ) ) ≤ f( ) + (1− ) ( ) for all between 0 and 1. In two
dimensions, this means if the points ( , ( )) and ( , ( )) are connected
with a straight line, the line is always above [or equal to] the function's
curve between those two points. If () is convex, then − () is concave.
◍ EM Algorithm.
Answer: Expectation-maximization algorithm.
◍ Pseudo-R-squared/Pseudo-R^2.
Answer: Measure similar to R^2 used for nonlinear regression models where
R^2 cannot be calculated.
◍ Autoregression.
Answer: Regression technique using past values of time series data as
predictors of future values.
◍ Binary Data.
Answer: Data that can take only two different values (true/false, 0/1,
black/white, on/off, etc.)
◍ Robust Solution.
Answer: A solution that whose worst-case outcome over all possible
scenarios is least bad.
◍ Convex Set.
Answer: A set of points for which a straight line drawn between any two
points in the set, stays inside the set. A circle is a convex set. A set shaped
, like the letter "U" is not convex; the line between the two points on top goes
outside of the set.
◍ False Negative (FN).
Answer: Data point that a model incorrectly classifies as not being in a
certain category. ("Negative" means the model classified it as not being in
the category, and "False" means the model's classification is incorrect.)
Sometimes abbreviated as "FN".
◍ Seasonality length/cycle length.
Answer: Fixed time period at which cycles/seasonalities repeat themselves.
◍ Correlation.
Answer: Relationship in which two things are likely to happen together,
regardless of whether one causes the other. (There is also a quantitative
statistical definition measuring the amount of correlation.)
◍ Missing data.
Answer: Values of data that are missing from a data set
◍ interarrival times.
Answer: The time between two consecutive arrivals of people, things, etc. --
for example, the time between consecutive phone calls to a service hotline.
◍ Greedy Algorithm.
Answer: Algorithm that makes the immediately-best choice at each step.
◍ Change Detection.
Answer: Identifying when a significant change has taken place in a process.
◍ Overfitting.
Answer: Building a model that describes random effects instead of or in
significant addition to the real effects; often caused by having too many
factors or parameters compared to the number of data points. Will have high
prediction errors.
◍ Decision Point.
Answer: Place in a simulation where there is a branch (or decision to be
