ISYEl 6501l Finall Examl (Latestl 2025/l 2026l Update)l Introductionl tol Analyticsl Modelingl Guidel |Questionsl &l Answers|l Gradel A|l 100%l Correctl (Verifiedl Solutions)
Q:l Binaryl Data
Answer:
Datal thatl canl takel onlyl twol differentl valuesl (true/false,l 0/1,l black/white,l on/off,l etc.)
Q:l Binaryl integerl program
Answer:
Integerl programl wherel alll variablesl arel binaryl variables.
Q:l Binaryl Variable
Answer:
Variablel thatl canl takel justl twol values:l 0l andl 1.
Q:l Binomiall Distribution
Answer:
Discretel probabilityl distributionl forl thel exactl numberl ofl successes,l k,l outl ofl al totall ofl nl iidl Bernoullil trials,l eachl withl probabilityl p:l Pr(�)=l (nl overl k)l p^k(1-p)^n-k
Q:l Blocking
Answer:
Factorl introducedl tol anl experimentall designl thatl interactsl withl thel effectl ofl thel factorsl tol bel studied.l Thel effectl ofl thel factorsl isl studiedl withinl thel samel levell (block)l ofl thel blockingl factor.
- / 4
Q:l boxl andl whiskerl plot
Answer:
Graphicall representationl datal showingl thel middlel rangel ofl datal (thel "box"),l reasonablel rangesl ofl variabilityl ("whiskers"),l andl pointsl (possiblel outliers)l outsidel thosel ranges.
Q:l Box-Coxl Transformation
Answer:
Transformationl ofl al non-normally-distributedl responsel tol al normall distribution.
Q:l Branching
Answer:
Splittingl al setl ofl datal intol twol orl morel subsets,l tol eachl bel analyzedl separately.
Q:l CART
Answer:
Classificationl andl regressionl trees.
Q:l Categoricall Data
Answer:
Datal thatl classifiesl observationsl withoutl quantitativel meaningl (forl example,l colorsl ofl cars)l orl wherel quantitativel amountsl arel categorizedl (forl example,l "0-10,l 11-20,l ...").
Q:l Causation
Answer:
Relationshipl inl whichl onel thingl makesl anotherl happenl (i.e.,l onel thingl causesl another).
Q:l Chancel Constraint
Answer:
Al probability-basedl constraint.l Forl example,l al standardl linearl constraintl mightl bel �x≤�.l Al similarl chancel constraintl mightl bel Prl (�x≤�)≥0.95
Q:l Changel Detection 2 / 4
Answer:
Identifyingl whenl al significantl changel hasl takenl placel inl al process.
Q:l Classification
Answer:
Thel separationl ofl datal intol twol orl morel categories,l orl (al point'sl classification)l thel categoryl al datal pointl isl putl into.
Q:l Classificationl tree
Answer:
Tree-basedl methodl forl classification.l Afterl branchingl tol splitl thel data,l eachl subsetl isl analyzedl withl itsl ownl classificationl model.
Q:l Classifier
Answer:
Al boundaryl thatl separatesl thel datal intol twol orl morel categories.l Alsol (morel generally)l anl algorithml thatl performsl classification.
Q:l Clique
Answer:
Al setl ofl nodesl wherel eachl pairl isl connectedl byl anl arc.
Q:l Cluster
Answer:
Al groupl ofl pointsl identifiedl asl near/similarl tol eachl other.
Q:l Clusterl Center
Answer:
Inl somel clusteringl algorithmsl (likel ��-meansl clustering),l thel centrall pointl (oftenl thel centroid)l ofl al clusterl ofl datal points.
Q:l Clustering
- / 4
Answer:
Separationl ofl datal pointsl intol groupsl ("clusters")l basedl onl nearness/similarityl tol eachl other.l Al commonl forml ofl unsupervisedl learning.
Q:l Collectivel outlier
Answer:
Al setl ofl datal pointsl thatl isl (uncommonly)l differentl froml othersl -l forl example,l al missingl heartbeatl inl anl electrocardiogram;l wel don'tl knowl exactlyl whichl millisecondl itl should'vel happenedl in,l butl collectivelyl there'sl al setl ofl millisecondsl thatl it'sl missingl from.
Q:l Concavel Function
Answer:
Al functionl f()l wherel forl everyl twol pointsl �l andl �,l �(�x+l (1−�)�)l ≥l ��(�)l +l (1−�)�(�)l forl alll �l betweenl 0l andl 1.l Inl twol dimensions,l thisl meansl ifl thel pointsl (�,�(�))l andl (�,�(�))l arel connectedl withl al straightl line,l thel linel isl alwaysl belowl [orl equall to]l thel function'sl curvel betweenl thosel twol points.l Ifl �()l isl concave,l thenl −�()l isl convex.
Q:l concordancel index
Answer:
Areal underl thel ROCl curve;l anl estimatel ofl thel classificationl model'sl accuracy.l Alsol calledl AUC.
Q:l Confusionl matrix
Answer:
Visualizationl ofl classificationl modell performance.
Q:l Constant
Answer:
Al numberl thatl remainsl thel same.
Q:l constraint
Answer:
- / 4