Isye 6501 Final exam
1-norm - ANS-Similar to rectilinear distance; measures the instantly-line period of a vector from
the beginning. If z=(z1,z2,...,zm) is a vector in an m-dimensional area, then it is 1-norm is
rectangular𝑧2directly-line length of a vector from the starting place. If z=(z1,z2,...,zm) is a vector
in an 𝑚-dimensional space, then its 2-norm is similar to 1-norm but everything is squared=
square root(Σm over i=1 (checking out two alternatives to see which one plays higher
Accuracy - ANS-Fraction of facts factors efficiently categorized via a model; same to TP+TN /
TP+FP+TN+FN
Action - ANS-In ARENA, something that is done to an entity.
Additive Seasonality - ANS-Seasonal effect this is delivered to a baseline value (as an example,
"the temperature in June is 10 degrees above the yearly baseline").
Adjusted R-squared - ANS-Variant of R2 that encourages easier models via penalizing using too
many variables.
AIC - ANS-Akaike records criterion- Model choice approach that trades off among version in
shape and model complexity. When comparing models, the version with lower AIC is favored.
Generally penalizes complexity less than BIC.
Algorithm - ANS-Step-by-step process designed to carry out a venture.
Analysis of Variance/ANOVA - ANS-Statistical method for dividing the version in observations
among different assets.
Approximate dynamic software - ANS-Dynamic programming model where the cost features are
approximated.
Arc - ANS-Connection between nodes/vertices in a network. In a community model, there's a
variable for each arc, same to the amount of float at the arc, and (optionally) a potential
constraint on the arc's waft. Also known as an facet.
Area under the curve (AUC) - ANS-Area underneath the ROC curve; an estimate of the
classification version's accuracy. Also called concordance index.
ARIMA - ANS-Autoregressive incorporated transferring average.
,Arrival Rate - ANS-Expected number of arrivals of people, things, and so forth. Consistent with
unit time -- for example, the anticipated wide variety of truck deliveries according to hour to a
warehouse.
Assignment Problem - ANS-Network optimization model with sets of nodes, that reveals the
first-class way to assign every node in one set to each node inside the different set.
Attribute - ANS-A function or size - for instance, someone's peak or the color of a car. Generally
interchangeable with "characteristic", and often with "covariate" or "predictor". In the same old
tabular format, a column of data.
Autoregression - ANS-Regression technique the use of past values of time series statistics as
predictors of future values.
Autoregressive incorporated transferring common (ARIMA) - ANS-Time collection model that
uses differences among observations whilst facts is nonstationary. Also referred to as
Box-Jenkins.
Backward elimination - ANS-Variable selection procedure that starts offevolved with all variables
after which iteratively eliminates the least-straight away-applicable variables from the model.
Balanced Design - ANS-Set of mixtures of aspect values across more than one elements, that
has the same quantity of runs for all combinations of tiers of one or greater factors.
Balking - ANS-An entity arrives to the queue, sees the size of the line (or some different
attribute), and decides to leave the device.
Bayes' theorem/Bayes' rule - ANS-Fundamental rule of conditional opportunity: 𝑃(𝐴selection
technique that trades off version healthy and model complexity. When evaluating models, the
model with lower BIC is desired. Generally penalizes complexity greater than AIC.
Bayesian Regression - ANS-Regression version that contains estimates of the way coefficients
and error are dispensed.
Bellman's Equation - ANS-Equation utilized in dynamic programming that ensures optimality of
a solution.
Bernoulli Distribution - ANS-Discrete opportunity distribution wherein the final results is binary,
either 0 or 1. Often, 1 represents achievement and zero represents failure. The opportunity of
the final results being 1 is 𝑝 and the opportunity of outcome being zero is 𝑞 = 1−𝑝, where 𝑝 is
between 0 and 1.
Bias - ANS-Systematic distinction between a true parameter of a populace and its estimate.
, Binary Data - ANS-Data that could take best one of a kind values (authentic/fake, zero/1,
black/white, on/off, and many others.)
Binary integer software - ANS-Integer program where all variables are binary variables.
Binary Variable - ANS-Variable which can take just two values: 0 and 1.
Binomial Distribution - ANS-Discrete opportunity distribution for the exact quantity of successes,
ok, out of a total of n iid Bernoulli trials, each with possibility p: Pr(𝑘)= (n over k) p^k(1-p)^n-k
Blocking - ANS-Factor brought to an experimental design that interacts with the effect of the
factors to be studied. The effect of the elements is studied in the identical level (block) of the
blocking component.
Box and whisker plot - ANS-Graphical illustration data displaying the middle variety of facts (the
"box"), affordable tiers of variability ("whiskers"), and points (viable outliers) outside those tiers.
Box-Cox Transformation - ANS-Transformation of a non-usually-allotted reaction to a normal
distribution.
Branching - ANS-Splitting a hard and fast of facts into two or extra subsets, to every be
analyzed one by one.
CART - ANS-Classification and regression trees.
Categorical Data - ANS-Data that classifies observations without quantitative that means (for
example, colorations of motors) or where quantitative quantities are labeled (as an example,
"0-10, eleven-20, ...").
Causation - ANS-Relationship wherein one issue makes another appear (i.E., one factor causes
any other).
Chance Constraint - ANS-A possibility-primarily based constraint. For example, a general linear
constraint is probably 𝐴x≤𝑏. A comparable chance constraint might be Pr (𝐴x≤𝑏)≥zero.95
Change Detection - ANS-Identifying while a sizable trade has taken vicinity in a process.
Classification - ANS-The separation of statistics into two or more categories, or (a factor's class)
the category a information factor is put into.
Classification tree - ANS-Tree-based totally method for class. After branching to break up the
records, each subset is analyzed with its own class version.
1-norm - ANS-Similar to rectilinear distance; measures the instantly-line period of a vector from
the beginning. If z=(z1,z2,...,zm) is a vector in an m-dimensional area, then it is 1-norm is
rectangular𝑧2directly-line length of a vector from the starting place. If z=(z1,z2,...,zm) is a vector
in an 𝑚-dimensional space, then its 2-norm is similar to 1-norm but everything is squared=
square root(Σm over i=1 (checking out two alternatives to see which one plays higher
Accuracy - ANS-Fraction of facts factors efficiently categorized via a model; same to TP+TN /
TP+FP+TN+FN
Action - ANS-In ARENA, something that is done to an entity.
Additive Seasonality - ANS-Seasonal effect this is delivered to a baseline value (as an example,
"the temperature in June is 10 degrees above the yearly baseline").
Adjusted R-squared - ANS-Variant of R2 that encourages easier models via penalizing using too
many variables.
AIC - ANS-Akaike records criterion- Model choice approach that trades off among version in
shape and model complexity. When comparing models, the version with lower AIC is favored.
Generally penalizes complexity less than BIC.
Algorithm - ANS-Step-by-step process designed to carry out a venture.
Analysis of Variance/ANOVA - ANS-Statistical method for dividing the version in observations
among different assets.
Approximate dynamic software - ANS-Dynamic programming model where the cost features are
approximated.
Arc - ANS-Connection between nodes/vertices in a network. In a community model, there's a
variable for each arc, same to the amount of float at the arc, and (optionally) a potential
constraint on the arc's waft. Also known as an facet.
Area under the curve (AUC) - ANS-Area underneath the ROC curve; an estimate of the
classification version's accuracy. Also called concordance index.
ARIMA - ANS-Autoregressive incorporated transferring average.
,Arrival Rate - ANS-Expected number of arrivals of people, things, and so forth. Consistent with
unit time -- for example, the anticipated wide variety of truck deliveries according to hour to a
warehouse.
Assignment Problem - ANS-Network optimization model with sets of nodes, that reveals the
first-class way to assign every node in one set to each node inside the different set.
Attribute - ANS-A function or size - for instance, someone's peak or the color of a car. Generally
interchangeable with "characteristic", and often with "covariate" or "predictor". In the same old
tabular format, a column of data.
Autoregression - ANS-Regression technique the use of past values of time series statistics as
predictors of future values.
Autoregressive incorporated transferring common (ARIMA) - ANS-Time collection model that
uses differences among observations whilst facts is nonstationary. Also referred to as
Box-Jenkins.
Backward elimination - ANS-Variable selection procedure that starts offevolved with all variables
after which iteratively eliminates the least-straight away-applicable variables from the model.
Balanced Design - ANS-Set of mixtures of aspect values across more than one elements, that
has the same quantity of runs for all combinations of tiers of one or greater factors.
Balking - ANS-An entity arrives to the queue, sees the size of the line (or some different
attribute), and decides to leave the device.
Bayes' theorem/Bayes' rule - ANS-Fundamental rule of conditional opportunity: 𝑃(𝐴selection
technique that trades off version healthy and model complexity. When evaluating models, the
model with lower BIC is desired. Generally penalizes complexity greater than AIC.
Bayesian Regression - ANS-Regression version that contains estimates of the way coefficients
and error are dispensed.
Bellman's Equation - ANS-Equation utilized in dynamic programming that ensures optimality of
a solution.
Bernoulli Distribution - ANS-Discrete opportunity distribution wherein the final results is binary,
either 0 or 1. Often, 1 represents achievement and zero represents failure. The opportunity of
the final results being 1 is 𝑝 and the opportunity of outcome being zero is 𝑞 = 1−𝑝, where 𝑝 is
between 0 and 1.
Bias - ANS-Systematic distinction between a true parameter of a populace and its estimate.
, Binary Data - ANS-Data that could take best one of a kind values (authentic/fake, zero/1,
black/white, on/off, and many others.)
Binary integer software - ANS-Integer program where all variables are binary variables.
Binary Variable - ANS-Variable which can take just two values: 0 and 1.
Binomial Distribution - ANS-Discrete opportunity distribution for the exact quantity of successes,
ok, out of a total of n iid Bernoulli trials, each with possibility p: Pr(𝑘)= (n over k) p^k(1-p)^n-k
Blocking - ANS-Factor brought to an experimental design that interacts with the effect of the
factors to be studied. The effect of the elements is studied in the identical level (block) of the
blocking component.
Box and whisker plot - ANS-Graphical illustration data displaying the middle variety of facts (the
"box"), affordable tiers of variability ("whiskers"), and points (viable outliers) outside those tiers.
Box-Cox Transformation - ANS-Transformation of a non-usually-allotted reaction to a normal
distribution.
Branching - ANS-Splitting a hard and fast of facts into two or extra subsets, to every be
analyzed one by one.
CART - ANS-Classification and regression trees.
Categorical Data - ANS-Data that classifies observations without quantitative that means (for
example, colorations of motors) or where quantitative quantities are labeled (as an example,
"0-10, eleven-20, ...").
Causation - ANS-Relationship wherein one issue makes another appear (i.E., one factor causes
any other).
Chance Constraint - ANS-A possibility-primarily based constraint. For example, a general linear
constraint is probably 𝐴x≤𝑏. A comparable chance constraint might be Pr (𝐴x≤𝑏)≥zero.95
Change Detection - ANS-Identifying while a sizable trade has taken vicinity in a process.
Classification - ANS-The separation of statistics into two or more categories, or (a factor's class)
the category a information factor is put into.
Classification tree - ANS-Tree-based totally method for class. After branching to break up the
records, each subset is analyzed with its own class version.
