ISYE 6501 FINAL EXAM UPDATED QUESTIONS
AND CORRECT 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.
AND CORRECT 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.
