ISYE 6501 EXAMS SCRIPT QUESTIONS AND
ANSWERS SET A+
✔✔p-value - ✔✔(1) In hypothesis testing, probability that results at least as extreme as
those in the data would be observed if the null hypothesis is true. (2) In regression,
probability that results at least as extreme as those in the data would be observed if the
coefficient of a variable is zero.
✔✔p-value fishing - ✔✔Testing many different hypotheses hoping to find one with a low
p-value. This is a bad practice; if enough things are tested, it's likely one will have a low
p-value due to randomness, but that doesn't mean it's a real effect.
✔✔Q-Q Plot - ✔✔Quantile-quantile plot -- a plot comparing the quantiles of two data
sets, or one data set and a distribution, to see whether they might have a common
distribution.
✔✔Quantitative Data - ✔✔Data that describes numerical amounts of something - for
example, height and weight.
✔✔Queue - ✔✔A line of people, things, etc. waiting to go through or be
processed/served by a resource -- for example, an airport security line.
✔✔Queuing - ✔✔The mathematical study of queues.
✔✔Random Effects - ✔✔Patterns that appear to occur in a subset of data, but only exist
due to random variability in the data and are not part of the system. (Note that there is a
different statistical definition for this phrase too.)
✔✔Random Forest - ✔✔Machine learning model that creates many different trees and
returns their mean output. Can be used with classification trees, regression trees,
decision trees.
, ✔✔Real Effects - ✔✔Actual patterns in the system being modeled. Ideally, good models
will reveal real effects.
✔✔Recall - ✔✔Fraction of data points in a certain category that are correctly classified
by a model; equal to TP/TP+FN; also called sensitivity, hit rate, and true positive rate
✔✔Receiver operating characteristic curve (ROC curve) - ✔✔Graph that plots the true
positive rate against the false positive rates for different classification cutoff thresholds.
✔✔Rectilinear distance - ✔✔The sum of the lengths in each dimension between two
points (the 1-norm distance). If 𝑥=(𝑥1,𝑥2,...,𝑥𝑚) and 𝑦=(𝑦1,𝑦2,...,𝑦𝑚) are two points in
an 𝑚-dimensional space, then the rectilinear distance between them is square root(
|𝑥1−𝑦1| + |𝑥2−𝑦2| +⋯+|𝑥𝑚−𝑦𝑚| ) = |𝑥1−𝑦1|+|𝑥2−𝑦2|+⋯+|𝑥𝑚−𝑦𝑚| = Σ m over i=1 |𝑥𝑖−𝑦𝑖|.
Also called Manhattan or 1-norm distance.
✔✔Regression - ✔✔Statistical model that describes relationships between variables,
and/or predicts future values of a response.
✔✔Regression Splines - ✔✔Regression model where different functions are used for
different ranges of the data.
✔✔Regression tree - ✔✔Tree-based method for regression. After branching to split the
data, each subset is analyzed with its own regression model.
✔✔regularization - ✔✔Addition of term(s) to the model to reduce model complexity or
overfitting. For example, adding a penalty to the objective function in regression can
help reduce overfitting (see ridge regression).
✔✔replication - ✔✔Running a stochastic simulation multiple times to sample the
distribution of possible simulation results. "A replication" also refers to a single one of
many runs of the simulation.
✔✔Resource - ✔✔In ARENA, the "doers" - for example, a call center worker at a queue.
✔✔Response - ✔✔A variable of interest that a model tries to estimate or predict.
✔✔Response surface - ✔✔Sequential experimentation strategy to understand the
relationship between response and input factors, and/or optimize the response.
✔✔Ridge Regression - ✔✔Method of regularization by limiting the sum of the squares
of the coefficients. Will reduce the magnitude of coefficients, not the number of variables
chosen.
ANSWERS SET A+
✔✔p-value - ✔✔(1) In hypothesis testing, probability that results at least as extreme as
those in the data would be observed if the null hypothesis is true. (2) In regression,
probability that results at least as extreme as those in the data would be observed if the
coefficient of a variable is zero.
✔✔p-value fishing - ✔✔Testing many different hypotheses hoping to find one with a low
p-value. This is a bad practice; if enough things are tested, it's likely one will have a low
p-value due to randomness, but that doesn't mean it's a real effect.
✔✔Q-Q Plot - ✔✔Quantile-quantile plot -- a plot comparing the quantiles of two data
sets, or one data set and a distribution, to see whether they might have a common
distribution.
✔✔Quantitative Data - ✔✔Data that describes numerical amounts of something - for
example, height and weight.
✔✔Queue - ✔✔A line of people, things, etc. waiting to go through or be
processed/served by a resource -- for example, an airport security line.
✔✔Queuing - ✔✔The mathematical study of queues.
✔✔Random Effects - ✔✔Patterns that appear to occur in a subset of data, but only exist
due to random variability in the data and are not part of the system. (Note that there is a
different statistical definition for this phrase too.)
✔✔Random Forest - ✔✔Machine learning model that creates many different trees and
returns their mean output. Can be used with classification trees, regression trees,
decision trees.
, ✔✔Real Effects - ✔✔Actual patterns in the system being modeled. Ideally, good models
will reveal real effects.
✔✔Recall - ✔✔Fraction of data points in a certain category that are correctly classified
by a model; equal to TP/TP+FN; also called sensitivity, hit rate, and true positive rate
✔✔Receiver operating characteristic curve (ROC curve) - ✔✔Graph that plots the true
positive rate against the false positive rates for different classification cutoff thresholds.
✔✔Rectilinear distance - ✔✔The sum of the lengths in each dimension between two
points (the 1-norm distance). If 𝑥=(𝑥1,𝑥2,...,𝑥𝑚) and 𝑦=(𝑦1,𝑦2,...,𝑦𝑚) are two points in
an 𝑚-dimensional space, then the rectilinear distance between them is square root(
|𝑥1−𝑦1| + |𝑥2−𝑦2| +⋯+|𝑥𝑚−𝑦𝑚| ) = |𝑥1−𝑦1|+|𝑥2−𝑦2|+⋯+|𝑥𝑚−𝑦𝑚| = Σ m over i=1 |𝑥𝑖−𝑦𝑖|.
Also called Manhattan or 1-norm distance.
✔✔Regression - ✔✔Statistical model that describes relationships between variables,
and/or predicts future values of a response.
✔✔Regression Splines - ✔✔Regression model where different functions are used for
different ranges of the data.
✔✔Regression tree - ✔✔Tree-based method for regression. After branching to split the
data, each subset is analyzed with its own regression model.
✔✔regularization - ✔✔Addition of term(s) to the model to reduce model complexity or
overfitting. For example, adding a penalty to the objective function in regression can
help reduce overfitting (see ridge regression).
✔✔replication - ✔✔Running a stochastic simulation multiple times to sample the
distribution of possible simulation results. "A replication" also refers to a single one of
many runs of the simulation.
✔✔Resource - ✔✔In ARENA, the "doers" - for example, a call center worker at a queue.
✔✔Response - ✔✔A variable of interest that a model tries to estimate or predict.
✔✔Response surface - ✔✔Sequential experimentation strategy to understand the
relationship between response and input factors, and/or optimize the response.
✔✔Ridge Regression - ✔✔Method of regularization by limiting the sum of the squares
of the coefficients. Will reduce the magnitude of coefficients, not the number of variables
chosen.
