ISYE 6501 Final TEST WITH CORRECT QUIZ AND
SOLUTIONS.
,1-norm - correct ans:Similar to rectilinear distance; measures the sum of the
lengths of each dimension of a vector from the origin. If 𝑧𝑧 1-1-norm - correct
ans:Similar to rectilinear distance; measures the sum of the lengths of each
dimension of a vector from the origin. norm - correct ans:Similar to rectilinear
distance; measures the sum of the lengths of each dimension of a vector
from the origin. = (𝑧𝑧1, 𝑧𝑧2, ... , 𝑧𝑧𝑚𝑚) is a vector in an 𝑚𝑚-dimensional
space, then its 1-norm is �|𝑧𝑧11-norm - correct ans:Similar to rectilinear
distance; measures the sum of the lengths of each dimension of a vector
from the origin. |1 + |𝑧𝑧2|1 + ⋯ + |𝑧𝑧𝑚𝑚| 1 1 = |𝑧𝑧1| + |𝑧𝑧2| + ⋯ + |𝑧𝑧| = ∑
|𝑧𝑧𝑖𝑖| 𝑚𝑚 𝑖𝑖=1 .
2-norm - correct ans:Similar to Euclidian distance; measures the straight-line
length of a vector from the origin. If 𝑧𝑧 = (𝑧𝑧1, 𝑧𝑧2, ... , 𝑧𝑧𝑚𝑚) is a vector in an
𝑚𝑚- dimensional space, then its 2-norm is �(𝑧𝑧1)2 + (𝑧𝑧2)2 + ⋯ + (𝑧𝑧𝑚𝑚)2 2
= �∑ (𝑧𝑧𝑖𝑖) 𝑚𝑚 2 𝑖𝑖=1 2 .
A/B testing - correct ans:Test of two alternatives to see if either one leads to
better outcomes.
Accuracy - correct ans:Fraction of data points correctly classified by a model;
equal to 𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹.
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/Adjusted R2 - correct ans:Variant of R2 that encourages
simpler models by penalizing the use of too many variables
AIC - correct ans:Akaike information criterion
,Akaike information criterion (AIC) - correct ans: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 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: 𝑃𝑃(𝐴𝐴|𝐵𝐵) = 𝑃𝑃(𝐵𝐵|𝐴𝐴)𝑃𝑃(𝐴𝐴) 𝑃𝑃(𝐵𝐵) .
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.
SOLUTIONS.
,1-norm - correct ans:Similar to rectilinear distance; measures the sum of the
lengths of each dimension of a vector from the origin. If 𝑧𝑧 1-1-norm - correct
ans:Similar to rectilinear distance; measures the sum of the lengths of each
dimension of a vector from the origin. norm - correct ans:Similar to rectilinear
distance; measures the sum of the lengths of each dimension of a vector
from the origin. = (𝑧𝑧1, 𝑧𝑧2, ... , 𝑧𝑧𝑚𝑚) is a vector in an 𝑚𝑚-dimensional
space, then its 1-norm is �|𝑧𝑧11-norm - correct ans:Similar to rectilinear
distance; measures the sum of the lengths of each dimension of a vector
from the origin. |1 + |𝑧𝑧2|1 + ⋯ + |𝑧𝑧𝑚𝑚| 1 1 = |𝑧𝑧1| + |𝑧𝑧2| + ⋯ + |𝑧𝑧| = ∑
|𝑧𝑧𝑖𝑖| 𝑚𝑚 𝑖𝑖=1 .
2-norm - correct ans:Similar to Euclidian distance; measures the straight-line
length of a vector from the origin. If 𝑧𝑧 = (𝑧𝑧1, 𝑧𝑧2, ... , 𝑧𝑧𝑚𝑚) is a vector in an
𝑚𝑚- dimensional space, then its 2-norm is �(𝑧𝑧1)2 + (𝑧𝑧2)2 + ⋯ + (𝑧𝑧𝑚𝑚)2 2
= �∑ (𝑧𝑧𝑖𝑖) 𝑚𝑚 2 𝑖𝑖=1 2 .
A/B testing - correct ans:Test of two alternatives to see if either one leads to
better outcomes.
Accuracy - correct ans:Fraction of data points correctly classified by a model;
equal to 𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹.
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/Adjusted R2 - correct ans:Variant of R2 that encourages
simpler models by penalizing the use of too many variables
AIC - correct ans:Akaike information criterion
,Akaike information criterion (AIC) - correct ans: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 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: 𝑃𝑃(𝐴𝐴|𝐵𝐵) = 𝑃𝑃(𝐵𝐵|𝐴𝐴)𝑃𝑃(𝐴𝐴) 𝑃𝑃(𝐵𝐵) .
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.
