ISYE 6501 EXAM SCRIPT VERIFIED
QUESTIONS WITH ACCURATE ANSWERS
●● A/B Testing
Answer: testing two alternatives to see which one performs better
●● 2-norm
Answer: Similar 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
Answer: Fraction of data points correctly classified by a model; equal to
TP+TN / TP+FP+TN+FN
●● Action
Answer: In ARENA, something that is done to an entity.
●● Additive Seasonality
Answer: Seasonal effect that is added to a baseline value (for example,
"the temperature in June is 10 degrees above the annual baseline").
,●● Adjusted R-squared
Answer: Variant of R2 that encourages simpler models by penalizing the
use of too many variables.
●● AIC
Answer: Akaike 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
Answer: Step-by-step procedure designed to carry out a task.
●● Analysis of Variance/ANOVA
Answer: Statistical method for dividing the variation in observations
among different sources.
●● Approximate dynamic program
Answer: Dynamic programming model where the value functions are
approximated.
●● Arc
Answer: Connection 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)
Answer: Area under the ROC curve; an estimate of the classification
model's accuracy. Also called concordance index.
●● ARIMA
Answer: Autoregressive integrated moving average.
●● Arrival Rate
Answer: Expected 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
Answer: Network 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
Answer: A 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
Answer: Regression technique using past values of time series data as
predictors of future values.
●● Autoregressive integrated moving average (ARIMA)
Answer: Time series model that uses differences between observations
when data is nonstationary. Also called Box-Jenkins.
●● Backward elimination
Answer: Variable selection process that starts with all variables and then
iteratively removes the least-immediately-relevant variables from the
model.
●● Balanced Design
Answer: Set 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
Answer: An 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
QUESTIONS WITH ACCURATE ANSWERS
●● A/B Testing
Answer: testing two alternatives to see which one performs better
●● 2-norm
Answer: Similar 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
Answer: Fraction of data points correctly classified by a model; equal to
TP+TN / TP+FP+TN+FN
●● Action
Answer: In ARENA, something that is done to an entity.
●● Additive Seasonality
Answer: Seasonal effect that is added to a baseline value (for example,
"the temperature in June is 10 degrees above the annual baseline").
,●● Adjusted R-squared
Answer: Variant of R2 that encourages simpler models by penalizing the
use of too many variables.
●● AIC
Answer: Akaike 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
Answer: Step-by-step procedure designed to carry out a task.
●● Analysis of Variance/ANOVA
Answer: Statistical method for dividing the variation in observations
among different sources.
●● Approximate dynamic program
Answer: Dynamic programming model where the value functions are
approximated.
●● Arc
Answer: Connection 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)
Answer: Area under the ROC curve; an estimate of the classification
model's accuracy. Also called concordance index.
●● ARIMA
Answer: Autoregressive integrated moving average.
●● Arrival Rate
Answer: Expected 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
Answer: Network 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
Answer: A 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
Answer: Regression technique using past values of time series data as
predictors of future values.
●● Autoregressive integrated moving average (ARIMA)
Answer: Time series model that uses differences between observations
when data is nonstationary. Also called Box-Jenkins.
●● Backward elimination
Answer: Variable selection process that starts with all variables and then
iteratively removes the least-immediately-relevant variables from the
model.
●● Balanced Design
Answer: Set 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
Answer: An 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
