ISYE 6501 FINAL EXAM QUESTIONS
& ANSWERS(SCORED A+)
1-norm - ANSWERSimilar to rectilinear distance; measures the straight-line length of
a vector from the origin. If z=(z1,z2,...,zm) is a vector in an m-dimensional space,
then it's 1-norm is square root(|𝑧1|+|𝑧2|+⋯+|𝑧𝑚| = |𝑧1|+|𝑧2|+⋯+|𝑧| = Σm over i=1 |𝑧𝑖|
A/B Testing - ANSWERtesting two alternatives to see which one performs better
2-norm - ANSWERSimilar to Euclidian distance; measures the straight-line length of
a vector from the origin. If z=(z1,z2,...,zm) is a vector in an 𝑚-dimensional space,
then its 2-norm is the same as 1-norm but everything is squared= square root(Σm
over i=1 (|𝑧𝑖|)^2)
Accuracy - ANSWERFraction of data points correctly classified by a model; equal to
TP+TN / TP+FP+TN+FN
Action - ANSWERIn ARENA, something that is done to an entity.
Additive Seasonality - ANSWERSeasonal effect that is added to a baseline value (for
example, "the temperature in June is 10 degrees above the annual baseline").
Adjusted R-squared - ANSWERVariant of R2 that encourages simpler models by
penalizing the use of too many variables.
AIC - ANSWERAkaike information criterion- 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 - ANSWERStep-by-step procedure designed to carry out a task.
Analysis of Variance/ANOVA - ANSWERStatistical method for dividing the variation
in observations among different sources.
Approximate dynamic program - ANSWERDynamic programming model where the
value functions are approximated.
Arc - ANSWERConnection 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 the curve (AUC) - ANSWERArea under the ROC curve; an estimate of
the classification model's accuracy. Also called concordance index.
ARIMA - ANSWERAutoregressive integrated moving average.
,Arrival Rate - ANSWERExpected 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 - ANSWERNetwork 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 - ANSWERA 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 - ANSWERRegression technique using past values of time series
data as predictors of future values.
Autoregressive integrated moving average (ARIMA) - ANSWERTime series model
that uses differences between observations when data is nonstationary. Also called
Box-Jenkins.
Backward elimination - ANSWERVariable selection process that starts with all
variables and then iteratively removes the least-immediately-relevant variables from
the model.
Balanced Design - ANSWERSet 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 - ANSWERAn 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 - ANSWERFundamental rule of conditional probability:
𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)
Bayesian Information criterion (BIC) - ANSWERModel 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 - ANSWERRegression model that incorporates estimates of
how coefficients and error are distributed.
Bellman's Equation - ANSWEREquation used in dynamic programming that ensures
optimality of a solution.
Bernoulli Distribution - ANSWERDiscrete 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 - ANSWERSystematic difference between a true parameter of a population and
its estimate.
, Binary Data - ANSWERData that can take only two different values (true/false, 0/1,
black/white, on/off, etc.)
Binary integer program - ANSWERInteger program where all variables are binary
variables.
Binary Variable - ANSWERVariable that can take just two values: 0 and 1.
Binomial Distribution - ANSWERDiscrete 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 - ANSWERFactor 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 - ANSWERGraphical 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 - ANSWERTransformation of a non-normally-distributed
response to a normal distribution.
Branching - ANSWERSplitting a set of data into two or more subsets, to each be
analyzed separately.
CART - ANSWERClassification and regression trees.
Categorical Data - ANSWERData that classifies observations without quantitative
meaning (for example, colors of cars) or where quantitative amounts are categorized
(for example, "0-10, 11-20, ...").
Causation - ANSWERRelationship in which one thing makes another happen (i.e.,
one thing causes another).
Chance Constraint - ANSWERA 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 - ANSWERIdentifying when a significant change has taken place
in a process.
Classification - ANSWERThe separation of data into two or more categories, or (a
point's classification) the category a data point is put into.
Classification tree - ANSWERTree-based method for classification. After branching
to split the data, each subset is analyzed with its own classification model.
Classifier - ANSWERA boundary that separates the data into two or more
categories. Also (more generally) an algorithm that performs classification.
& ANSWERS(SCORED A+)
1-norm - ANSWERSimilar to rectilinear distance; measures the straight-line length of
a vector from the origin. If z=(z1,z2,...,zm) is a vector in an m-dimensional space,
then it's 1-norm is square root(|𝑧1|+|𝑧2|+⋯+|𝑧𝑚| = |𝑧1|+|𝑧2|+⋯+|𝑧| = Σm over i=1 |𝑧𝑖|
A/B Testing - ANSWERtesting two alternatives to see which one performs better
2-norm - ANSWERSimilar to Euclidian distance; measures the straight-line length of
a vector from the origin. If z=(z1,z2,...,zm) is a vector in an 𝑚-dimensional space,
then its 2-norm is the same as 1-norm but everything is squared= square root(Σm
over i=1 (|𝑧𝑖|)^2)
Accuracy - ANSWERFraction of data points correctly classified by a model; equal to
TP+TN / TP+FP+TN+FN
Action - ANSWERIn ARENA, something that is done to an entity.
Additive Seasonality - ANSWERSeasonal effect that is added to a baseline value (for
example, "the temperature in June is 10 degrees above the annual baseline").
Adjusted R-squared - ANSWERVariant of R2 that encourages simpler models by
penalizing the use of too many variables.
AIC - ANSWERAkaike information criterion- 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 - ANSWERStep-by-step procedure designed to carry out a task.
Analysis of Variance/ANOVA - ANSWERStatistical method for dividing the variation
in observations among different sources.
Approximate dynamic program - ANSWERDynamic programming model where the
value functions are approximated.
Arc - ANSWERConnection 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 the curve (AUC) - ANSWERArea under the ROC curve; an estimate of
the classification model's accuracy. Also called concordance index.
ARIMA - ANSWERAutoregressive integrated moving average.
,Arrival Rate - ANSWERExpected 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 - ANSWERNetwork 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 - ANSWERA 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 - ANSWERRegression technique using past values of time series
data as predictors of future values.
Autoregressive integrated moving average (ARIMA) - ANSWERTime series model
that uses differences between observations when data is nonstationary. Also called
Box-Jenkins.
Backward elimination - ANSWERVariable selection process that starts with all
variables and then iteratively removes the least-immediately-relevant variables from
the model.
Balanced Design - ANSWERSet 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 - ANSWERAn 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 - ANSWERFundamental rule of conditional probability:
𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)
Bayesian Information criterion (BIC) - ANSWERModel 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 - ANSWERRegression model that incorporates estimates of
how coefficients and error are distributed.
Bellman's Equation - ANSWEREquation used in dynamic programming that ensures
optimality of a solution.
Bernoulli Distribution - ANSWERDiscrete 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 - ANSWERSystematic difference between a true parameter of a population and
its estimate.
, Binary Data - ANSWERData that can take only two different values (true/false, 0/1,
black/white, on/off, etc.)
Binary integer program - ANSWERInteger program where all variables are binary
variables.
Binary Variable - ANSWERVariable that can take just two values: 0 and 1.
Binomial Distribution - ANSWERDiscrete 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 - ANSWERFactor 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 - ANSWERGraphical 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 - ANSWERTransformation of a non-normally-distributed
response to a normal distribution.
Branching - ANSWERSplitting a set of data into two or more subsets, to each be
analyzed separately.
CART - ANSWERClassification and regression trees.
Categorical Data - ANSWERData that classifies observations without quantitative
meaning (for example, colors of cars) or where quantitative amounts are categorized
(for example, "0-10, 11-20, ...").
Causation - ANSWERRelationship in which one thing makes another happen (i.e.,
one thing causes another).
Chance Constraint - ANSWERA 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 - ANSWERIdentifying when a significant change has taken place
in a process.
Classification - ANSWERThe separation of data into two or more categories, or (a
point's classification) the category a data point is put into.
Classification tree - ANSWERTree-based method for classification. After branching
to split the data, each subset is analyzed with its own classification model.
Classifier - ANSWERA boundary that separates the data into two or more
categories. Also (more generally) an algorithm that performs classification.
