ISYE 6501 FINAL STUDY
GUIDE WITH QUESTION
AND ANSWERS
,1-norm - correct ans:Similar 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 - correct ans:testing two alternatives to see which one performs
better
2-norm - correct ans: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 - correct ans:Fraction of data points correctly classified by a model;
equal to TP+TN / TP+FP+TN+FN
Action - correct ans:In ARENA, something that is done to an entity.
Additive Seasonality - correct ans: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 - correct ans:Variant of R2 that encourages simpler
models by penalizing the use of too many variables.
AIC - correct ans: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 - correct ans:Step-by-step procedure designed to carry out a task.
,Analysis of Variance/ANOVA - correct ans:Statistical method for dividing the
variation in observations among different sources.
Approximate dynamic program - correct ans:Dynamic programming model
where the value functions are approximated.
Arc - correct ans: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) - correct ans:Area under the ROC curve; an
estimate of the classification model's accuracy. Also called concordance
index.
ARIMA - correct ans:Autoregressive integrated moving average.
Arrival Rate - correct ans: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 - correct ans: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 - correct ans: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 - correct ans:Regression technique using past values of time
series data as predictors of future values.
Autoregressive integrated moving average (ARIMA) - correct ans:Time series
model that uses differences between observations when data is
nonstationary. Also called Box-Jenkins.
Backward elimination - correct ans:Variable selection process that starts with
all variables and then iteratively removes the least-immediately-relevant
variables from the model.
Balanced Design - correct ans: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 - correct ans: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 - correct ans:Fundamental rule of conditional
probability: 𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)
Bayesian Information criterion (BIC) - correct ans:Model 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 - correct ans:Regression model that incorporates
estimates of how coefficients and error are distributed.
Bellman's Equation - correct ans:Equation used in dynamic programming that
ensures optimality of a solution.
GUIDE WITH QUESTION
AND ANSWERS
,1-norm - correct ans:Similar 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 - correct ans:testing two alternatives to see which one performs
better
2-norm - correct ans: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 - correct ans:Fraction of data points correctly classified by a model;
equal to TP+TN / TP+FP+TN+FN
Action - correct ans:In ARENA, something that is done to an entity.
Additive Seasonality - correct ans: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 - correct ans:Variant of R2 that encourages simpler
models by penalizing the use of too many variables.
AIC - correct ans: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 - correct ans:Step-by-step procedure designed to carry out a task.
,Analysis of Variance/ANOVA - correct ans:Statistical method for dividing the
variation in observations among different sources.
Approximate dynamic program - correct ans:Dynamic programming model
where the value functions are approximated.
Arc - correct ans: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) - correct ans:Area under the ROC curve; an
estimate of the classification model's accuracy. Also called concordance
index.
ARIMA - correct ans:Autoregressive integrated moving average.
Arrival Rate - correct ans: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 - correct ans: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 - correct ans: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 - correct ans:Regression technique using past values of time
series data as predictors of future values.
Autoregressive integrated moving average (ARIMA) - correct ans:Time series
model that uses differences between observations when data is
nonstationary. Also called Box-Jenkins.
Backward elimination - correct ans:Variable selection process that starts with
all variables and then iteratively removes the least-immediately-relevant
variables from the model.
Balanced Design - correct ans: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 - correct ans: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 - correct ans:Fundamental rule of conditional
probability: 𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)
Bayesian Information criterion (BIC) - correct ans:Model 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 - correct ans:Regression model that incorporates
estimates of how coefficients and error are distributed.
Bellman's Equation - correct ans:Equation used in dynamic programming that
ensures optimality of a solution.
