ISYE 6501 QUESTIONS AND ANSWERS SET A+
✔✔p-value (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
pvalue. (Bad practice.)
✔✔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.
✔✔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.
✔✔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 𝑇𝑇/𝑇𝑇+𝐹𝐹; 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. Also called the 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. Also called spline regression.
✔✔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.
✔✔Robust solution - ✔✔A solution that whose worst-case outcome over all possible
scenarios is least bad.
✔✔ROC curve - ✔✔Receiver operating characteristic curve.
✔✔Root - ✔✔The first, complete data set in a tree model.
✔✔R-squared/R2 - ✔✔Measure of linear regression model quality, the fraction of
variance in the response that is explained by the model.
✔✔p-value (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
pvalue. (Bad practice.)
✔✔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.
✔✔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.
✔✔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 𝑇𝑇/𝑇𝑇+𝐹𝐹; 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. Also called the 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. Also called spline regression.
✔✔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.
✔✔Robust solution - ✔✔A solution that whose worst-case outcome over all possible
scenarios is least bad.
✔✔ROC curve - ✔✔Receiver operating characteristic curve.
✔✔Root - ✔✔The first, complete data set in a tree model.
✔✔R-squared/R2 - ✔✔Measure of linear regression model quality, the fraction of
variance in the response that is explained by the model.
