ISYE 6501 COMPREHENSIVE QUESTIONS AND
ANSWERS SET A+
✔✔Discrete-event simulation - ✔✔A simulation that models a system that changes
when specific events occur.
✔✔Distance - ✔✔How far it is between two points -- but there are different ways to
measure it (see Minkowski distance).
✔✔Distribution-fitting - ✔✔Determining whether a set of data seems to follow a certain
probability distribution, or determining which of several distributions the data is close to.
✔✔Double exponential smoothing - ✔✔Two-parameter exponential smoothing
technique that incorporates trend.
✔✔Dynamic programming - ✔✔Optimization approach that involves making a sequence
of decisions over time, based on the current state of a system.
✔✔Earth - ✔✔Name of many implementations of multi-adaptive regression spline
(MARS) model, because "MARS" is a trademark.
✔✔Edge - ✔✔Connection between two nodes/vertices in a network. In a network
model, there is a variable for each edge, equal to the amount of flow on the arc, and
(optionally) a capacity constraint on the edge's flow. Also called an arc.
✔✔Eigenvalue - ✔✔Amount by which an eigenvector gets rescaled in a linear
transformation.
✔✔Eigenvector - ✔✔Non-zero vector that does not change direction when a linear
transformation is applied to it, but only gets rescaled by the eigenvalue
✔✔Elastic Net - ✔✔Combination of lasso and ridge regression.
,✔✔Elbow Diagram - ✔✔A graph of improvement in function value as something else
(e.g.,number of clusters) increases or decreases; the spot where improvement levels
out
✔✔EM Algorithm - ✔✔Expectation-maximization algorithm.
✔✔Emperical Bayes Model - ✔✔Model that uses Bayes' theorem to update an initial
guess/distribution based on observed data.
✔✔Entity - ✔✔A person/thing moving through a simulation.
✔✔Error(per data point) - ✔✔The difference (or absolute difference, squared difference,
or other measure) between the estimate of a piece of data and its true value.
✔✔Error(total over data set) - ✔✔The total of all errors in a data set.
✔✔Euclidian distance/straight-line distance - ✔✔The length of a straight line (the 2-
norm distance) between two points. If 𝑥=(𝑥1,𝑥2,...,𝑥𝑚) and 𝑦=(𝑦1,𝑦2,...,𝑦𝑚) are two
points in an 𝑚-dimensional space, is the square root(
(x1−𝑦1)^2+(𝑥2−𝑦2)^2+⋯+(𝑥𝑚−𝑦𝑚)^2 )=square root( Σm over i=1(𝑥𝑖−𝑦𝑖)^2)
✔✔Expectation-maximization algorithm (EM algorithm) - ✔✔General description of an
algorithm with two steps (often iterated), one that finds the function for the expected
likelihood of getting the response given current parameters, and one that finds new
parameter values to maximize that probability.
✔✔exploitation - ✔✔Using known information to get good outcomes.
✔✔Exploration - ✔✔Finding new/better/more information to determine how to optimize
output.
✔✔Exponential Distribution - ✔✔A continuous probability distribution of the time
between events: 𝑓(𝑥)=𝜆𝑒^−𝜆x. If the number of events in a fixed time follows the
Poission distribution, then the time between them has the exponential distribution. The
exponential distribution has the memoryless property.
✔✔Exponential smoothing - ✔✔Data smoothing technique in which older observations
are assigned exponentially decresing weights, so more emphasis is given to recent
observations.
✔✔Factorial Design - ✔✔Tests of different combinations of factor values over multiple
factors, to find each one's effect, and interaction effects, on the outcome.
, ✔✔Fall out - ✔✔Fraction of data points not in a certain category that are incorrectly
classified by a model; equal to FP / TN+FP Also called false positive rate.
✔✔False Negative (FN) - ✔✔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".
✔✔False Negative Rate - ✔✔Fraction of data points in a certain category that are
incorrectly classified by a model; equal to FN / TP+FN. Also called miss rate.
✔✔False Positive (FP) - ✔✔Data point that a model incorrectly classifies as being in a
certain category. ("Positive" means the model classified it as being in the category, and
"False" means the model's classification is incorrect.) Sometimes abbreviated as "FP".
✔✔False Positive Rate - ✔✔Fraction of data points not in a certain category that are
incorrectly classified by a model; equal to FP / TN+FP . Also called fall out.
✔✔False Omission Rate - ✔✔Fraction of data points the model classifies as not in a
certain category, that are really in the category; equal to FN / TN+FN.
✔✔feasible solution - ✔✔A solution that satisfies a set of constraints.
✔✔Feature - ✔✔(1) A characteristic or measurement - for example, a person's height or
the color of a car. Generally interchangeable with "attribute", and often with "covariate"
or "predictor". In the standard tabular format, a column of data. Also called an attribute.
(2) A combination of attributes in a specific format - for example, 0.5×height plus
7×shoe-size.
✔✔FIFO - ✔✔First-in, first-out: The first entity to join a queue is the first one to come out
-- for example, a supermarket checkout line.
✔✔Fitting - ✔✔Finding a model (including, if appropriate, a probability distribution) that
is a good description of real effects in a set of data. The model is sometimes called a
"fit".
✔✔Fixed Charge - ✔✔In optimization models, a cost that depends only on whether
something happens, but not how much - for example, a transaction cost for buying or
selling stock that is the same regardless of how many shares are bought or sold.
✔✔Flow - ✔✔In a network model, the amount sent from one node to another along an
arc. In network models, 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.
ANSWERS SET A+
✔✔Discrete-event simulation - ✔✔A simulation that models a system that changes
when specific events occur.
✔✔Distance - ✔✔How far it is between two points -- but there are different ways to
measure it (see Minkowski distance).
✔✔Distribution-fitting - ✔✔Determining whether a set of data seems to follow a certain
probability distribution, or determining which of several distributions the data is close to.
✔✔Double exponential smoothing - ✔✔Two-parameter exponential smoothing
technique that incorporates trend.
✔✔Dynamic programming - ✔✔Optimization approach that involves making a sequence
of decisions over time, based on the current state of a system.
✔✔Earth - ✔✔Name of many implementations of multi-adaptive regression spline
(MARS) model, because "MARS" is a trademark.
✔✔Edge - ✔✔Connection between two nodes/vertices in a network. In a network
model, there is a variable for each edge, equal to the amount of flow on the arc, and
(optionally) a capacity constraint on the edge's flow. Also called an arc.
✔✔Eigenvalue - ✔✔Amount by which an eigenvector gets rescaled in a linear
transformation.
✔✔Eigenvector - ✔✔Non-zero vector that does not change direction when a linear
transformation is applied to it, but only gets rescaled by the eigenvalue
✔✔Elastic Net - ✔✔Combination of lasso and ridge regression.
,✔✔Elbow Diagram - ✔✔A graph of improvement in function value as something else
(e.g.,number of clusters) increases or decreases; the spot where improvement levels
out
✔✔EM Algorithm - ✔✔Expectation-maximization algorithm.
✔✔Emperical Bayes Model - ✔✔Model that uses Bayes' theorem to update an initial
guess/distribution based on observed data.
✔✔Entity - ✔✔A person/thing moving through a simulation.
✔✔Error(per data point) - ✔✔The difference (or absolute difference, squared difference,
or other measure) between the estimate of a piece of data and its true value.
✔✔Error(total over data set) - ✔✔The total of all errors in a data set.
✔✔Euclidian distance/straight-line distance - ✔✔The length of a straight line (the 2-
norm distance) between two points. If 𝑥=(𝑥1,𝑥2,...,𝑥𝑚) and 𝑦=(𝑦1,𝑦2,...,𝑦𝑚) are two
points in an 𝑚-dimensional space, is the square root(
(x1−𝑦1)^2+(𝑥2−𝑦2)^2+⋯+(𝑥𝑚−𝑦𝑚)^2 )=square root( Σm over i=1(𝑥𝑖−𝑦𝑖)^2)
✔✔Expectation-maximization algorithm (EM algorithm) - ✔✔General description of an
algorithm with two steps (often iterated), one that finds the function for the expected
likelihood of getting the response given current parameters, and one that finds new
parameter values to maximize that probability.
✔✔exploitation - ✔✔Using known information to get good outcomes.
✔✔Exploration - ✔✔Finding new/better/more information to determine how to optimize
output.
✔✔Exponential Distribution - ✔✔A continuous probability distribution of the time
between events: 𝑓(𝑥)=𝜆𝑒^−𝜆x. If the number of events in a fixed time follows the
Poission distribution, then the time between them has the exponential distribution. The
exponential distribution has the memoryless property.
✔✔Exponential smoothing - ✔✔Data smoothing technique in which older observations
are assigned exponentially decresing weights, so more emphasis is given to recent
observations.
✔✔Factorial Design - ✔✔Tests of different combinations of factor values over multiple
factors, to find each one's effect, and interaction effects, on the outcome.
, ✔✔Fall out - ✔✔Fraction of data points not in a certain category that are incorrectly
classified by a model; equal to FP / TN+FP Also called false positive rate.
✔✔False Negative (FN) - ✔✔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".
✔✔False Negative Rate - ✔✔Fraction of data points in a certain category that are
incorrectly classified by a model; equal to FN / TP+FN. Also called miss rate.
✔✔False Positive (FP) - ✔✔Data point that a model incorrectly classifies as being in a
certain category. ("Positive" means the model classified it as being in the category, and
"False" means the model's classification is incorrect.) Sometimes abbreviated as "FP".
✔✔False Positive Rate - ✔✔Fraction of data points not in a certain category that are
incorrectly classified by a model; equal to FP / TN+FP . Also called fall out.
✔✔False Omission Rate - ✔✔Fraction of data points the model classifies as not in a
certain category, that are really in the category; equal to FN / TN+FN.
✔✔feasible solution - ✔✔A solution that satisfies a set of constraints.
✔✔Feature - ✔✔(1) A characteristic or measurement - for example, a person's height or
the color of a car. Generally interchangeable with "attribute", and often with "covariate"
or "predictor". In the standard tabular format, a column of data. Also called an attribute.
(2) A combination of attributes in a specific format - for example, 0.5×height plus
7×shoe-size.
✔✔FIFO - ✔✔First-in, first-out: The first entity to join a queue is the first one to come out
-- for example, a supermarket checkout line.
✔✔Fitting - ✔✔Finding a model (including, if appropriate, a probability distribution) that
is a good description of real effects in a set of data. The model is sometimes called a
"fit".
✔✔Fixed Charge - ✔✔In optimization models, a cost that depends only on whether
something happens, but not how much - for example, a transaction cost for buying or
selling stock that is the same regardless of how many shares are bought or sold.
✔✔Flow - ✔✔In a network model, the amount sent from one node to another along an
arc. In network models, 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.
