ISYE 6501 EVALUATION TEST QUESTIONS AND
ANSWERS SET A+
✔✔Bernoulli Distribution - ✔✔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 𝑝 and the probability of outcome being 0 is 𝑞 =
1−𝑝, where 𝑝 is between 0 and 1.
✔✔Bias - ✔✔Systematic difference between a true parameter of a population and its
estimate.
✔✔Binary Data - ✔✔Data that can take only two different values (true/false, 0/1,
black/white, on/off, etc.)
✔✔Binary integer program - ✔✔Integer program where all variables are binary
variables.
✔✔Binary Variable - ✔✔Variable that can take just two values: 0 and 1.
✔✔Binomial Distribution - ✔✔Discrete probability distribution for the exact number of
successes, k, out of a total of n iid Bernoulli trials, each with probability p: Pr(𝑘)= (n over
k) p^k(1-p)^n-k
✔✔Blocking - ✔✔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 - ✔✔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 - ✔✔Transformation of a non-normally-distributed response
to a normal distribution.
✔✔Branching - ✔✔Splitting a set of data into two or more subsets, to each be analyzed
separately.
✔✔CART - ✔✔Classification and regression trees.
✔✔Categorical Data - ✔✔Data that classifies observations without quantitative meaning
(for example, colors of cars) or where quantitative amounts are categorized (for
example, "0-10, 11-20, ...").
✔✔Causation - ✔✔Relationship in which one thing makes another happen (i.e., one
thing causes another).
✔✔Chance Constraint - ✔✔A probability-based constraint. For example, a standard
linear constraint might be 𝐴x≤𝑏. A similar chance constraint might be Pr (𝐴x≤𝑏)≥0.95
✔✔Change Detection - ✔✔Identifying when a significant change has taken place in a
process.
✔✔Classification - ✔✔The separation of data into two or more categories, or (a point's
classification) the category a data point is put into.
✔✔Classification tree - ✔✔Tree-based method for classification. After branching to split
the data, each subset is analyzed with its own classification model.
✔✔Classifier - ✔✔A boundary that separates the data into two or more categories. Also
(more generally) an algorithm that performs classification.
✔✔Clique - ✔✔A set of nodes where each pair is connected by an arc.
✔✔Cluster - ✔✔A group of points identified as near/similar to each other.
✔✔Cluster Center - ✔✔In some clustering algorithms (like 𝑘𝑘-means clustering), the
central point (often the centroid) of a cluster of data points.
✔✔Clustering - ✔✔Separation of data points into groups ("clusters") based on
nearness/similarity to each other. A common form of unsupervised learning.
✔✔Collective outlier - ✔✔A set of data points that is (uncommonly) different from others
- for example, a missing heartbeat in an electrocardiogram; we don't know exactly which
millisecond it should've happened in, but collectively there's a set of milliseconds that it's
missing from.
, ✔✔Concave Function - ✔✔A function f() where for every two points 𝑥 and 𝑦, 𝑓(𝑐x+
(1−𝑐)𝑦) ≥ 𝑐𝑓(𝑥) + (1−𝑐)𝑓(𝑦) for all 𝑐 between 0 and 1. In two dimensions, this means if
the points (𝑥,𝑓(𝑥)) and (𝑦,𝑓(𝑦)) are connected with a straight line, the line is always
below [or equal to] the function's curve between those two points. If 𝑓() is concave, then
−𝑓() is convex.
✔✔concordance index - ✔✔Area under the ROC curve; an estimate of the classification
model's accuracy. Also called AUC.
✔✔Confusion matrix - ✔✔Visualization of classification model performance.
✔✔Constant - ✔✔A number that remains the same.
✔✔constraint - ✔✔Part of an optimization model that describes a restriction on the
solution (the values of the variables).
✔✔Contextual outlier - ✔✔A data point that is (uncommonly) far from other data points
related to it - for example, in Atlanta, a 90-degree (Fahrenheit) day in winter is an
outlier, but a 90-degree day in summer is not.
✔✔continuous-time simulation - ✔✔A simulation that models a system continuously, at
every instant of time; continuous-time simulation models are often based on differential
equations.
✔✔Control - ✔✔(1) A variable whose value remains constant for all runs of an
experiment, so changes in this variable don't affect the experiment. (2) Design an
experiment where some factors ("controls" by definition (1)) are held constant to avoid
them affecting the outcome.
✔✔Convex function - ✔✔A function f() where for every two points 𝑥 and 𝑦, 𝑓(𝑐x+ (1−𝑐)𝑦)
≤ 𝑐f(𝑥) + (1−𝑐)𝑓(𝑦) for all 𝑐 between 0 and 1. In two dimensions, this means if the points
(𝑥,𝑓(𝑥)) and (𝑦,𝑓(𝑦)) are connected with a straight line, the line is always above [or equal
to] the function's curve between those two points. If 𝑓() is convex, then −𝑓() is concave.
✔✔Convex Hull (of a set of points) - ✔✔Smallest convex shape that the set of points is
contained in.
✔✔Convex Optimization model - ✔✔An optimization model where the objective function
is to minimize a convex function (or maximize a concave function) and the constraints
define a convex set of feasible solutions.
✔✔Convex Quadratic Function - ✔✔A second-order polynomial function that is convex.
ANSWERS SET A+
✔✔Bernoulli Distribution - ✔✔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 𝑝 and the probability of outcome being 0 is 𝑞 =
1−𝑝, where 𝑝 is between 0 and 1.
✔✔Bias - ✔✔Systematic difference between a true parameter of a population and its
estimate.
✔✔Binary Data - ✔✔Data that can take only two different values (true/false, 0/1,
black/white, on/off, etc.)
✔✔Binary integer program - ✔✔Integer program where all variables are binary
variables.
✔✔Binary Variable - ✔✔Variable that can take just two values: 0 and 1.
✔✔Binomial Distribution - ✔✔Discrete probability distribution for the exact number of
successes, k, out of a total of n iid Bernoulli trials, each with probability p: Pr(𝑘)= (n over
k) p^k(1-p)^n-k
✔✔Blocking - ✔✔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 - ✔✔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 - ✔✔Transformation of a non-normally-distributed response
to a normal distribution.
✔✔Branching - ✔✔Splitting a set of data into two or more subsets, to each be analyzed
separately.
✔✔CART - ✔✔Classification and regression trees.
✔✔Categorical Data - ✔✔Data that classifies observations without quantitative meaning
(for example, colors of cars) or where quantitative amounts are categorized (for
example, "0-10, 11-20, ...").
✔✔Causation - ✔✔Relationship in which one thing makes another happen (i.e., one
thing causes another).
✔✔Chance Constraint - ✔✔A probability-based constraint. For example, a standard
linear constraint might be 𝐴x≤𝑏. A similar chance constraint might be Pr (𝐴x≤𝑏)≥0.95
✔✔Change Detection - ✔✔Identifying when a significant change has taken place in a
process.
✔✔Classification - ✔✔The separation of data into two or more categories, or (a point's
classification) the category a data point is put into.
✔✔Classification tree - ✔✔Tree-based method for classification. After branching to split
the data, each subset is analyzed with its own classification model.
✔✔Classifier - ✔✔A boundary that separates the data into two or more categories. Also
(more generally) an algorithm that performs classification.
✔✔Clique - ✔✔A set of nodes where each pair is connected by an arc.
✔✔Cluster - ✔✔A group of points identified as near/similar to each other.
✔✔Cluster Center - ✔✔In some clustering algorithms (like 𝑘𝑘-means clustering), the
central point (often the centroid) of a cluster of data points.
✔✔Clustering - ✔✔Separation of data points into groups ("clusters") based on
nearness/similarity to each other. A common form of unsupervised learning.
✔✔Collective outlier - ✔✔A set of data points that is (uncommonly) different from others
- for example, a missing heartbeat in an electrocardiogram; we don't know exactly which
millisecond it should've happened in, but collectively there's a set of milliseconds that it's
missing from.
, ✔✔Concave Function - ✔✔A function f() where for every two points 𝑥 and 𝑦, 𝑓(𝑐x+
(1−𝑐)𝑦) ≥ 𝑐𝑓(𝑥) + (1−𝑐)𝑓(𝑦) for all 𝑐 between 0 and 1. In two dimensions, this means if
the points (𝑥,𝑓(𝑥)) and (𝑦,𝑓(𝑦)) are connected with a straight line, the line is always
below [or equal to] the function's curve between those two points. If 𝑓() is concave, then
−𝑓() is convex.
✔✔concordance index - ✔✔Area under the ROC curve; an estimate of the classification
model's accuracy. Also called AUC.
✔✔Confusion matrix - ✔✔Visualization of classification model performance.
✔✔Constant - ✔✔A number that remains the same.
✔✔constraint - ✔✔Part of an optimization model that describes a restriction on the
solution (the values of the variables).
✔✔Contextual outlier - ✔✔A data point that is (uncommonly) far from other data points
related to it - for example, in Atlanta, a 90-degree (Fahrenheit) day in winter is an
outlier, but a 90-degree day in summer is not.
✔✔continuous-time simulation - ✔✔A simulation that models a system continuously, at
every instant of time; continuous-time simulation models are often based on differential
equations.
✔✔Control - ✔✔(1) A variable whose value remains constant for all runs of an
experiment, so changes in this variable don't affect the experiment. (2) Design an
experiment where some factors ("controls" by definition (1)) are held constant to avoid
them affecting the outcome.
✔✔Convex function - ✔✔A function f() where for every two points 𝑥 and 𝑦, 𝑓(𝑐x+ (1−𝑐)𝑦)
≤ 𝑐f(𝑥) + (1−𝑐)𝑓(𝑦) for all 𝑐 between 0 and 1. In two dimensions, this means if the points
(𝑥,𝑓(𝑥)) and (𝑦,𝑓(𝑦)) are connected with a straight line, the line is always above [or equal
to] the function's curve between those two points. If 𝑓() is convex, then −𝑓() is concave.
✔✔Convex Hull (of a set of points) - ✔✔Smallest convex shape that the set of points is
contained in.
✔✔Convex Optimization model - ✔✔An optimization model where the objective function
is to minimize a convex function (or maximize a concave function) and the constraints
define a convex set of feasible solutions.
✔✔Convex Quadratic Function - ✔✔A second-order polynomial function that is convex.
