ISYE 6501 FINAL EXAM
PREPARATION QUESTIONS
WITH CORRECT ANSWERS
Margin - Answer-For a single point, the distance between the point and the classification
boundary; for a set of points, the minimum distance between a point in the set and the
classification boundary. Also called the separation.
Markov chain - Answer-Process where a system changes its state in a way that
depends only on its current state.
Markov decision process - Answer-Markov chain model where decisions are made at
some states, and state transitions have associated rewards.
MARS (Multi-adaptive regression splines.) - Answer-Specific regression spline model
that has become commonly-used. Also called "earth".
Mathematical Programming - Answer-Mathematical optimization, often using variables,
constraints, and objective function.
Maximization Problem - Answer-Optimization model where the objective is to find the
feasible solution that maximizes the value of the objective function.
Maximum Flow Problem - Answer-Network optimization model that finds the most flow
that can be sent from one specific node to another.
maximum likelihood - Answer-A method that finds the set of parameter values for which
a model is most likely to generate the actual values of the data.
McNemar's test - Answer-Nonparametric test for comparing paired samples where the
output is yes/no (or A/B, or 0/1, etc.).
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.
Memoryless (Markov chain) - Answer-Property that the next state of the system is
dependent only on the current state, not any previous states.
,Minimization Problem - Answer-Optimization model where the objective is to find the
feasible solution that minimizes the value of the objective function.
Minkowski distance (of order 𝑝) - Answer-The 𝑝-norm distance between two points. If
𝑥=(𝑥1,𝑥2,...,𝑥𝑚) and 𝑦=(𝑦1,𝑦2,...,𝑦𝑚) are two points in an 𝑚-dimensional space, p-root
(|𝑥1−𝑦1|^𝑝+|𝑥2−𝑦2|^𝑝+⋯+|xm−ym|^𝑝) = p-root( Σ m over i=1 |𝑥𝑖−𝑦𝑖|^p)
misclassified - Answer-Put into the wrong category by a classifier.
miss rate - Answer-Fraction of data points in a certain category that are incorrectly
classified by a model; equal to FN/ TP+FN. Also called false negative rate.
Missing data - Answer-Values of data that are missing from a data set
Mixed strategy/randomized strategy - Answer-A strategy where a participant's action is
determined randomly according to probabilities
Model (mathematical) - Answer-A mathematical description of a system. Because real-
life systems are complex, mathematical models of them are only approximate. In
analytics, the term "model" is used in at least three different ways: (1) A general type of
mathematical approach, like "regression"; (2) A general type of mathematical approach
with specific parameters, like "regression using credit score and income as predictors";
(3) A general type of mathematical approach with specific parameters and values for the
parameters, like "regression, with the prediction equal to 100,000, plus 100 times credit
score, plus 3 times income".
Modularity - Answer-Measure of the density of connections between communicates in a
network.
Module - Answer-In ARENA, a building-block of a simulation, or the process, resource,
etc. it represents.
Most Optimal - Answer-Please don't say this (or "more optimal"). "Optimal" means
"best", and "most best" or "more best" are not proper English.
Moving Average - Answer-Smoothing technique that replaces data values with the
mean of a number of consecutive observed values.
multi-armed bandit - Answer-Model that allows the tradeoff between exploration of
unknown resources and exploitation of known resources to optimize output.
Multiplicative seasonality - Answer-Seasonal effect that is multiplied by a baseline value
(for example, "the temperature in June is 20% higher than the annual baseline").
Multiplier - Answer-A term that something is multiplied by. For example, to change units
from meters to centimeters, the multiplier is 100.
, Negative likelihood ratio - Answer-Ratio of the fraction of data points in a certain
category that are misclassified as not in the cateogry, to the fraction of data points not in
the category that are correctly classified as not being in the category; equal to (1-
sensitivity)/specificity = (FN/(FN+TP)) / (TN/(TN+FP))
Negative predictive value - Answer-Fraction of data points classified as not in a certain
category that are really not in that category; equal to TN / TN+FN
Network - Answer-Model where locations (nodes or vertices) are connected by arcs or
edges, with flow on the arcs from node to node.
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.
Neural network - Answer-A machine learning model that itself is modeled after the
workings of neurons in the brain.
node - Answer-Location in a network. In a network model, there is a constraint for each
node to ensure that the incoming flow equals the outgoing flow. Also called a vertex.
non-convex program - Answer-Optimization model where the constraint set is not
convex, and/or the objective function is to minimize a nonconvex function or to
maximize a nonconcave function.
non-negativity constraints - Answer-Constraints that require variables to be greater than
or equal to zero.
Non-parametric tests - Answer-Statistical test that makes no assumptions about the
population distribution from which the data is sampled. Often focus on the median.
norm/distance norm - Answer-A function that measures the size/length of a vector and
satisfies some basic technical properties that are beyond the scope of this course. In
this course, we focus on Minkowski norm (or p-norm).
Normal distribution - Answer-Continuous probability distribution: 𝑓(𝑥)=(1/𝜎*√2𝜋) *
𝑒^−(𝑥−𝜇)^𝜎^2. Model error is often assumed to be normally distributed (for example,
in linear regression).
objective function - Answer-Part of an optimization model that measures the quality of a
solution (the values of the variables).
PREPARATION QUESTIONS
WITH CORRECT ANSWERS
Margin - Answer-For a single point, the distance between the point and the classification
boundary; for a set of points, the minimum distance between a point in the set and the
classification boundary. Also called the separation.
Markov chain - Answer-Process where a system changes its state in a way that
depends only on its current state.
Markov decision process - Answer-Markov chain model where decisions are made at
some states, and state transitions have associated rewards.
MARS (Multi-adaptive regression splines.) - Answer-Specific regression spline model
that has become commonly-used. Also called "earth".
Mathematical Programming - Answer-Mathematical optimization, often using variables,
constraints, and objective function.
Maximization Problem - Answer-Optimization model where the objective is to find the
feasible solution that maximizes the value of the objective function.
Maximum Flow Problem - Answer-Network optimization model that finds the most flow
that can be sent from one specific node to another.
maximum likelihood - Answer-A method that finds the set of parameter values for which
a model is most likely to generate the actual values of the data.
McNemar's test - Answer-Nonparametric test for comparing paired samples where the
output is yes/no (or A/B, or 0/1, etc.).
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.
Memoryless (Markov chain) - Answer-Property that the next state of the system is
dependent only on the current state, not any previous states.
,Minimization Problem - Answer-Optimization model where the objective is to find the
feasible solution that minimizes the value of the objective function.
Minkowski distance (of order 𝑝) - Answer-The 𝑝-norm distance between two points. If
𝑥=(𝑥1,𝑥2,...,𝑥𝑚) and 𝑦=(𝑦1,𝑦2,...,𝑦𝑚) are two points in an 𝑚-dimensional space, p-root
(|𝑥1−𝑦1|^𝑝+|𝑥2−𝑦2|^𝑝+⋯+|xm−ym|^𝑝) = p-root( Σ m over i=1 |𝑥𝑖−𝑦𝑖|^p)
misclassified - Answer-Put into the wrong category by a classifier.
miss rate - Answer-Fraction of data points in a certain category that are incorrectly
classified by a model; equal to FN/ TP+FN. Also called false negative rate.
Missing data - Answer-Values of data that are missing from a data set
Mixed strategy/randomized strategy - Answer-A strategy where a participant's action is
determined randomly according to probabilities
Model (mathematical) - Answer-A mathematical description of a system. Because real-
life systems are complex, mathematical models of them are only approximate. In
analytics, the term "model" is used in at least three different ways: (1) A general type of
mathematical approach, like "regression"; (2) A general type of mathematical approach
with specific parameters, like "regression using credit score and income as predictors";
(3) A general type of mathematical approach with specific parameters and values for the
parameters, like "regression, with the prediction equal to 100,000, plus 100 times credit
score, plus 3 times income".
Modularity - Answer-Measure of the density of connections between communicates in a
network.
Module - Answer-In ARENA, a building-block of a simulation, or the process, resource,
etc. it represents.
Most Optimal - Answer-Please don't say this (or "more optimal"). "Optimal" means
"best", and "most best" or "more best" are not proper English.
Moving Average - Answer-Smoothing technique that replaces data values with the
mean of a number of consecutive observed values.
multi-armed bandit - Answer-Model that allows the tradeoff between exploration of
unknown resources and exploitation of known resources to optimize output.
Multiplicative seasonality - Answer-Seasonal effect that is multiplied by a baseline value
(for example, "the temperature in June is 20% higher than the annual baseline").
Multiplier - Answer-A term that something is multiplied by. For example, to change units
from meters to centimeters, the multiplier is 100.
, Negative likelihood ratio - Answer-Ratio of the fraction of data points in a certain
category that are misclassified as not in the cateogry, to the fraction of data points not in
the category that are correctly classified as not being in the category; equal to (1-
sensitivity)/specificity = (FN/(FN+TP)) / (TN/(TN+FP))
Negative predictive value - Answer-Fraction of data points classified as not in a certain
category that are really not in that category; equal to TN / TN+FN
Network - Answer-Model where locations (nodes or vertices) are connected by arcs or
edges, with flow on the arcs from node to node.
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.
Neural network - Answer-A machine learning model that itself is modeled after the
workings of neurons in the brain.
node - Answer-Location in a network. In a network model, there is a constraint for each
node to ensure that the incoming flow equals the outgoing flow. Also called a vertex.
non-convex program - Answer-Optimization model where the constraint set is not
convex, and/or the objective function is to minimize a nonconvex function or to
maximize a nonconcave function.
non-negativity constraints - Answer-Constraints that require variables to be greater than
or equal to zero.
Non-parametric tests - Answer-Statistical test that makes no assumptions about the
population distribution from which the data is sampled. Often focus on the median.
norm/distance norm - Answer-A function that measures the size/length of a vector and
satisfies some basic technical properties that are beyond the scope of this course. In
this course, we focus on Minkowski norm (or p-norm).
Normal distribution - Answer-Continuous probability distribution: 𝑓(𝑥)=(1/𝜎*√2𝜋) *
𝑒^−(𝑥−𝜇)^𝜎^2. Model error is often assumed to be normally distributed (for example,
in linear regression).
objective function - Answer-Part of an optimization model that measures the quality of a
solution (the values of the variables).
