r statistical application development exploratory analysis

r statistical application development exploratory analysis

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时间:2018-02-10

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1、4ExploratoryAnalysisTukey(1977)inhisbenchmarkbookExploratoryDataAnalysis,abbreviatedpopularlyasEDA,describes"bestmethods"as:Wedonotguaranteetointroduceyoutothe"best"tools,particularlysincewearenotsurethattherecanbeuniquebests.Thegoalofthischapteristoemp

2、hasizeonEDAanditsstrengths.Inthepreviouschapter,wehaveseenvisualizationtechniquesfordataofdifferentcharacteristics.AnalyticalinsightisalsoimportantandthischapterconsidersEDAtechniques.Further,themorepopularmeasuresincludethemean,standarderror,andsoon.It

3、hasbeenprovedmanytimesthatthemeanhasseveraldrawbacks;oneofthembeingthatitisverysensitivetooutliers/extremes.Thus,inexploratoryanalysisthefocusisonmeasureswhicharerobusttotheextremes.ManytechniquesconsideredinthischapterarediscussedinmoredetailbyVelleman

4、andHoaglin(1981),andaneBookhasbeenkindlymadeavailableathttp://dspace.library.cornell.edu/handle/1813/62.Inthefirstsection,wewillhaveapeekattheoftenusedmeasuresforexploratoryanalysis.Themainlearningsfromthischapterarelistedasfollows:Summarystatisticsbase

5、donmediananditsvariants,whicharerobusttooutliersVisualizationtechniquesinstem-and-leaf,lettervalues,andbagplotsFirstregressionmodelinResistantlineandrefinedmethodsinsmoothingdataandmedianpolishExploratoryAnalysisEssentialsummarystatisticsWehaveseenusefu

6、lsummarystatisticsofmeanandvarianceintheDiscretedistributionsandContinuousdistributionssectionsofChapter1,DataCharacteristics.Theconceptsthereinhavetheirownutilityvalue.Thedrawbackofsuchstatisticalmetricsisthattheyareverysensitivetooutliers,inthesenseth

7、atasingleobservationmaycompletelydistorttheentirestory.Inthissection,wediscusssomeexploratoryanalysismetricswhichareintuitiveandmorerobustthanthemetricssuchasmeanandvariance.Percentiles,quantiles,andmedianForagivendatasetandanumber0

8、ledividesthedatasetintotwopartitionswith100k%ofthevaluesbelowitand100(1-k)percentofthevaluesaboveit.Thefractionkisreferredasaquantile.InStatistics,quantilesareusedmoreoftenthanpercentiles.Thedifferencebeingthatthequantilesvaryove

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