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ISYE 6501 Final Exam With Correct Solutions 2024
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ISYE 6501 Final Exam With Correct Solutions 2024
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ISYE 6501 Final Exam With CorrectSolutions 2024
1-norm .- .correct .answer.Similar .to .rectilinear .distance; .measures .the .sum .of .the .lengths
.of .each .dimension .of .a .vector .from .the .origin. .If .𝑧𝑧 .= .(𝑧𝑧1, .𝑧𝑧2, .... ., .𝑧𝑧𝑚𝑚) .is .a .vector .in .an
.𝑚𝑚-dimensional .space, .then .its .1-norm .is .�|𝑧𝑧1|1 .+ .|𝑧𝑧2|1 .+ .⋯ .+ .|𝑧𝑧𝑚𝑚| .1 .1 .= .|𝑧𝑧1| .+
.|𝑧𝑧2| .+ .⋯ .+ .|𝑧𝑧| .= .∑ .|𝑧𝑧𝑖𝑖| .𝑚𝑚 .𝑖𝑖=1 ..
2-norm .- .correct .answer.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 .answer.Test .of .two .alternatives .to .see .if .either .one .leads .to .better
.outcomes.
Accuracy .- .correct .answer.Fraction .of .data .points .correctly .classified .by .a .model; .equal .to
.𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇 .𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹.
Action .- .correct .answer.In .ARENA, .something .that .is .done .to .an .entity.
Additive .seasonality .- .correct .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/Adjusted .R2 .- .correct .answer.Variant .of .R2 .that .encourages .simpler
.models .by .penalizing .the .use .of .too .many .variables
AIC .- .correct .answer.Akaike .information .criterion
Akaike .information .criterion .(AIC) .- .correct .answer.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 .answer.Step-by-step .procedure .designed .to .carry .out .a .task.
Analysis .of .Variance/ANOVA .- .correct .answer.Statistical .method .for .dividing .the .variation
.in .observations .among .different .sources.
,Approximate .dynamic .program .- .correct .answer.Dynamic .programming .model .where .the
.value .functions .are .approximated.
Arc .- .correct .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 .curve/AUC .- .correct .answer.Area .under .the .ROC .curve; .an .estimate .of .the
.classification .model's .accuracy. .Also .called .concordance .index.
ARIMA .- .correct .answer.Autoregressive .integrated .moving .average.
Arrival .rate .- .correct .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 .- .correct .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 .- .correct .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 .- .correct .answer.Regression .technique .using .past .values .of .time .series
.data .as .predictors .of .future .values.
Autoregressive .integrated .moving .average .(ARIMA) .- .correct .answer.Time .series .model
.that .uses .differences .between .observations .when .data .is .nonstationary. .Also .called .Box-
Jenkins
Backward .elimination .- .correct .answer.Variable .selection .process .that .starts .with .all
.variables .and .then .iteratively .removes .the .least-immediately-relevant .variables .from .the
.model.
Balanced .design .- .correct .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 .- .correct .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 .- .correct .answer.Fundamental .rule .of .conditional .probability:
.𝑃𝑃(𝐴𝐴|𝐵𝐵) .= .𝑃𝑃(𝐵𝐵|𝐴𝐴)𝑃𝑃(𝐴𝐴) .𝑃𝑃(𝐵𝐵) ..
, Bayesian .Information .criterion .(BIC) .- .correct .answer.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 .answer.Regression .model .that .incorporates .estimates .of
.how .coefficients .and .error .are .distributed.
Bellman's .equation .- .correct .answer.Equation .used .in .dynamic .programming .that
.ensures .optimality .of .a .solution.
Bernoulli .distribution .- .correct .answer.Discrete .probability .distribution .where .the .outcome
.is .binary, .either .0 .or .1. .Often, .1 .represents .success .and .0 .represents .failure. .The
.probability .of .the .outcome .being .1 .is .𝑝𝑝 .and .the .probability .of .outcome .being .0 .is .𝑞𝑞 .= .1 .−
.𝑝𝑝, .where .𝑝𝑝 .is .between .0 .and .1.
Bias .- .correct .answer.Systematic .difference .between .a .true .parameter .of .a .population
.and .its .estimate
BIC .- .correct .answer.Bayesian .information .criterion
Binary .data .- .correct .answer.Data .that .can .take .only .two .different .values .(true/false, .0/1,
.black/white, .on/off, .etc.).
Binary .integer .program .- .correct .answer.Integer .program .where .all .variables .are .binary
.variables.
Binary .variable .- .correct .answer.Variable .that .can .take .just .two .values: .0 .and .1.
Binomial .distribution .- .correct .answer.Discrete .probability .distribution .for .the .exact
.number .of .successes, .k, .out .of .a .total .of .n .iid .Bernoulli .trials, .each .with .probability .p:
.Pr(𝑘𝑘) .= .� .𝑛𝑛 .𝑘𝑘� .𝑝𝑝𝑘𝑘(1 .− .𝑝𝑝)𝑛𝑛−𝑘𝑘.
Blocking .- .correct .answer.Factor .introduced .to .an .experimental .design .that .interacts .with
.the .effect .of .the .factors .to .be .studied. .The .effect .of .the .factors .is .studied .within .the .same
.level .(block) .of .the .blocking .factor
Box .and .whisker .plot .- .correct .answer.Graphical .representation .data .showing .the .middle
.range .of .data .(the ."box"), .reasonable .ranges .of .variability .("whiskers"), .and .points
.(possible .outliers) .outside .those .ranges.
Box-Cox .transformation .- .correct .answer.Transformation .of .a .non-normally-distributed
.response .to .a .normal .distribution.
Branching .- .correct .answer.Splitting .a .set .of .data .into .two .or .more .subsets, .to .each .be
.analyzed .separately.
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