AnhangAA.
1GrundlagenthemenMchtemaneinenEinstiegindiemoderneDatenanalysendenoderlehren,soisteinberblicküberGrundlagenthemensinnvoll.
DieseThemenerscheinenzumEinstiegrat-sam:1.
Kap.
1Statistikheute2.
Kap.
3Rstarten3.
Kap.
4Erstkontakt4.
Abschn.
5.
1berblicküberdiewichtigstenObjekttypen5.
Kap.
6Datenimportund-export6.
Kap.
7Datenjudo7.
Kap.
8DeskriptiveStatistik8.
Kap.
11GrundlagenderDatenvisualisierungmitggplot29.
Kap.
15GrundlagendesModellierens10.
Kap.
16Inferenzstatistik11.
Kap.
18LineareModelle12.
Kap.
24TextminingNatürlichkannesimEinzelfallsinnvollsein,nichtalleAbschnittedieserKapitelzulernenbzw.
lehren.
SoknntemanzumBeispielaufdieAbschn.
7.
5und7.
6verzichten.
LetztlichisteineAuswahleinStückweitsubjektiv.
DieseAuswahlberücksichtigtgrundlegendeThemenzuR,derDatenanalyse,derInferenzstatistik,desstatistischenModellierensso-wiemoderne"AspektewieTextmining.
DieübrigenKapiteldiesesBuchessinddemnachfortgeschrittenereThemen.
AmanspruchsvollstensinddieKapiteldesTeilsRahmen2:27,28und29.
541SpringerFachmedienWiesbadenGmbH,einTeilvonSpringerNature2019S.
Sauer,ModerneDatenanalysemitR,FOM-Edition,https://doi.
org/10.
1007/978-3-658-21587-3542AnhangAA.
2bungsaufgabenDiefolgendenAussagensindentwederalsrichtig"oderalsfalsch"zubewerten.
OffeneFragenverlangeneinenTextalsAntwort.
A.
2.
1Fragen1.
Beiinstall.
packagesspieltderParameterdependencies=TRUEinderPraxiskeineRolle.
2.
DateienmitderEndung.
RsindkeineTextdateien.
3.
DerBefehlread.
csvkannauchDateieneinlesen,dienichtlokal,sondernaufeinemServerimInternetgespeichertsind.
4.
FehlendeWertewerdeninRdurchNAkodiert.
5.
UmVariableneinenWertzuzuweisen,kannmaninRdenZuweisungspfeil0istdieWahrscheinlichkeitkleinerals50%,dassdasmodellierteEreigniseintritt,wennalleanderenPrdiktoren0sind.
31.
DielogistischeRegressionsolltenichtverwendetwerden,wenndieabhngigeVari-abledichotomist.
32.
DielogistischeRegressionstelltdenZusammenhangzwischenPrdiktorundKrite-riumnichtmiteinerGeraden,sondernmiteiners-frmigenKurvedar.
33.
BevordieKoefzientenderlogistischenRegressionalsOddsRatiointerpretiertwer-denknnen,müssensiedelogarithmiertwerden.
34.
Unterdelogarithmierenverstehtman,dieUmkehrfunktiondere-FunktionaufeineGleichunganzuwenden.
35.
WendetmandienormaleRegressionan,umeinedichotomeVariablealsKriteriumzumodellieren,sokannmanWahrscheinlichkeitengrerals1undkleinerals0bekommen.
36.
EinetypischeIdeederClusteranalyselautet,dieVarianzinnerhalbderClusterjeweilszumaximieren.
37.
Beieinerk-means-ClusteranalysedarfmannichtdieAnzahlderClustervorabfest-legen;vielmehrermitteltderAlgorithmusdierichtigeAnzahlderCluster.
38.
FürdieWahlderrichtigen"AnzahlderClusterkanndasEllbogen-KriteriumalsEntscheidungsgrundlageherangezogenwerden.
39.
EinScreeplotstelltdieVarianzinnerhalbderClusteralsFunktionderAnzahlderClusterdar(imRahmenderClusteranalyse).
40.
DieeuklidischeDistanzzwischenzweiObjekteninderEbenelsstsichmitdemSatzdesPythagorasberechnen.
544AnhangAA.
2.
2Lsungen1.
Falsch2.
Falsch3.
Richtig4.
Richtig5.
Richtig6.
Falsch7.
Richtig8.
Falsch9.
Falsch10.
Richtig11.
Falsch12.
Falsch13.
Falsch14.
Richtig15.
Richtig16.
Falsch17.
Richtig18.
AuffehlendeWerteprüfen,FllemitfehlendenWertenlschen,fehlendeWerteggf.
ersetzen,nachFehlernsuchen,Ausreieridentizieren,hochkorrelierteVariablenn-den,z-standardisieren,Quasi-Konstantenden,aufNormalverteilungprüfen,Werteumkodierenundbinnen".
19.
Richtig20.
Richtig21.
Richtig22.
EsgehthierumvierDatenstzemitzweiVariablen(Spalten;XundY).
OffenbarsinddieDatenstzepraktischidentisch:AlleXhabendengleichenMittelwertunddiegleicheVarianz;dasselbegiltfürdieY.
DieKorrelationzwischenXundYistinallenvierDatenstzengleich.
AllerdingserzhlteineVisualisierungdervierDatenstzeeineganzandereGeschichte.
23.
Falsch24.
Richtig25.
Richtig26.
Falsch27.
Richtig28.
Falsch29.
Richtig30.
Richtig31.
Falsch32.
FalschAnhangA54533.
Richtig34.
Richtig35.
Falsch.
Richtigwre:DieUmkehrfunktiondesLogarithmus,alsodiee-Funktion,aufeineGleichunganzuwenden.
36.
Richtig37.
Falsch38.
Falsch.
Richtigwre:MangibtdieAnzahlderClustervor.
DannvergleichtmandieVarianzinnerhalbderverschiedenenLsungen.
39.
Richtig40.
Richtig41.
RichtigA.
2.
3HinweiseRStudioundShinysindeingetrageneMarkennamenvonRStudio,Inc.
A.
2.
4Beispiel-CurriculumFüreineEinführungsveranstaltungknntederStoffwiefolgtinzwlfTerminezujedreiZeit-Stundenaufgeteiltwerden(s.
Abschn.
A.
1).
MitdiesemZeitumfangkannnureinTeildesBuchesvermitteltwerden.
Tab.
A.
1ZuordnungvonThemenzuTerminenTerminThema(Kapitel)1Organisatorisches1Einführung1ArbeitenmitR1Dateneinlesen2Datenjudo2DeskriptiveStatistik3Datenvisualisieren4Fallstudie(z.
B.
zumovies")5Datenmodellieren6Inferenzstatistik7LineareRegression–metrisch8LineareRegression–kategorial9Fallstudie(z.
B.
zutitanic")10Clusteranalyse11Textmining12WiederholungTerminThema(Kapitel)1Organisatorisches1Einführung1ArbeitenmitR1Dateneinlesen2Datenjudo2DeskriptiveStatistik3Datenvisualisieren4Fallstudie(z.
B.
zumovies")5Datenmodellieren6Inferenzstatistik7LineareRegression–metrisch8LineareRegression–kategorial9Fallstudie(z.
B.
zutitanic")10Clusteranalyse11Textmining12Wiederholung546AnhangAA.
3VerwendeteR-PaketeDiefolgendenR-PaketewurdenindiesemBuchverwendet:Stanley(2018),MoreyundRouder(2015),Beaujean(2012),CantyundRipley(2017),GerlancundKirby(2015),Robinson(2018),FoxundWeisberg(2011),JedWingetal.
(2018),Maechleretal.
(2018),WeiundSimko(2017),Jackson(2016),Wilke(2017),Wickhametal.
(2018),Analytics&Weston(2017),Wickhametal.
(2017),WinstonChang(2014),RenundRussell(2016),Schloerkeetal.
(2017),KahleundWick-ham(2013),Jeppsonetal.
(2017),Wickham(2016),Wickham(2015),Arnold(2018),GesmannunddeCastillo(2011),Warnesetal.
(2016),Auguie(2017),Hugh-Jones(2018),BuchtaundHornik(2017),Zhu(2018),Xie(2018),Meschiari(2015),Chengetal.
(2017),BryerundSpeerschneider(2016),Wild(2015),Navarro(2015),BacheundWickham(2014),W.
N.
VenablesundRipley(2002),vanBuurenundGroothuis-Oudshoorn(2011),Wickham(2017a),Pruimetal.
(2017),Raiche(2010),DarócziundTsegelskyi(2017),AustundBarth(2017),HothornundZeileis(2015),Ooms(2018),Soetaert(2017),Sachs(2017),Urbanek(2013),Sauer(2017a),Sauer(2018),Robinetal.
(2011),Wickham(2018a),Revelle(2018),Champely(2018),Neuwirth(2014),HenryundWickham(2018),TherneauundAtkinson(2018),South(2011),Wickham(2018b),VanDerWaletal.
(2014),Pebesma(2018),McNamaraetal.
(2018),Bouchet-Valat(2014),Hlavac(2018),Wickham(2018c),WickhamundHenry(2018),Wickham(2017b),Gentry(2015),KowarikundTempl(2016),Garnier(2018),RamundWickham(2016),Fellows(2014),Dahl(2016),Sauer(2017b).
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506Repositorium,504,509Reproduzierbarkeit,17,504,507Resampling,259,308Residuen,329Residuum,Fehler,324Richtigkeit,Korrektklassikationsrate,Accuracy,388RidgeRegression,416RMarkdown,479robust,257ROC-Kurve,ROC,357R-Skript,29SSchtzer,306Screeplot,440,446Sentiment,464Sentimentanalyse,465Sentimentlexikon,460Shape-Daten,217signalnoiseratio,289Signal,Phnomen,9Signkanz,271Signikanz,270Signikanzniveau,272Simulation,305Skalenniveau,104Skriptfenster,22,37SQLite-Datenbanken,471Standardabweichung,SD,105Standardfehler,273,304,306Standardnormalverteilung,133Standardwerte,DefaultValues,Voreinstellung,40Steigung,323Stichprobenverteilung,272,304,305stochastisch,251Stop-Kriterien,391Strafterm,416Streuungsmae,105Stufen,Faktorstufen,levels,51SupportVectorMachines,414Syntax,Code,13TThemes,Themen,195Tibble,tbl-df,tbl,68TidyData,DateninNormalform,3Tidytext-Dataframe,450Token,Term,450Transformieren,94Trendgerade,Regressionsgerade,322Trunkieren,Stemming,457Tuningparameter,384,395,414Twitter,463Uberanpassung,255bergewissheit,Overcertainty,426Umkodieren,136UngeleitetesModellieren,253univariat,103Unquoting,Unzitieren,533Unteranpassung,Undertting,255Urhorde,496VVariable,38variableimportance,398Variablen,Merkmale,8,37Variablenrelevanz,398Variablenwichtigkeit,variableimportance,426Varianz,Var,105Vektor,42,48vektoriell,42vektoriellesVerarbeiten,vektoriellesRechnen,42vektorisiert,515Vorhersagefehler,324,325Vorhersagen,252WWert,8YYAML,Yaml,YAML-Header,482Youden-Index,355ZZellen,Elemente,8zentrieren,337Zielgre,Output-Variable,Kriterium,Ausgabegre,250Zitieren,Quotation,Backen,529Zufallszahlen,set.
seed(),409Zusammenfassungsfunktionen,87
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