英文商務統(tǒng)計學-Ch02課件

BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-1Chapter2PresentingDatainTablesandChartsBusinessStatistics:AFirstCourse

FifthEditionBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-2LearningObjectivesInthischapteryoulearn:

TodeveloptablesandchartsforcategoricaldataTodeveloptablesandchartsfornumericaldataTheprinciplesofproperlypresentinggraphsBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-3CategoricalDataAreSummarizedByTables&GraphsCategoricalDataGraphingDataPieChartsParetoChartBarChartsTabulatingData

SummaryTableBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-4OrganizingCategoricalData:SummaryTableAsummarytableindicatesthefrequency,amount,orpercentageofitemsinasetofcategoriessothatyoucanseedifferencesbetweencategories.

BankingPreference?PercentATM16%Automatedorlivetelephone2%Drive-throughserviceatbranch17%Inpersonatbranch41%Internet24%BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-5BarandPieChartsBarchartsandPiechartsareoftenusedforcategoricaldataLengthofbarorsizeofpiesliceshowsthefrequencyorpercentageforeachcategoryBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-6OrganizingCategoricalData:

BarChartInabarchart,abarshowseachcategory,thelengthofwhichrepresentstheamount,frequencyorpercentageofvaluesfallingintoacategory.

BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-7OrganizingCategoricalData:

PieChartThepiechartisacirclebrokenupintoslicesthatrepresentcategories.Thesizeofeachsliceofthepievariesaccordingtothepercentageineachcategory.

BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-8OrganizingCategoricalData:

ParetoChartUsedtoportraycategoricaldata(nominalscale)Averticalbarchart,wherecategoriesareshownindescendingorderoffrequencyAcumulativepolygonisshowninthesamegraphUsedtoseparatethe“vitalfew”fromthe“trivialmany”BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-9OrganizingCategoricalData:

ParetoChartBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-10TablesandChartsfor

NumericalDataNumericalDataOrderedArrayStem-and-LeafDisplayHistogramPolygonOgiveFrequencyDistributionsandCumulativeDistributionsBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-11OrganizingNumericalData:

OrderedArrayAnorderedarrayisasequenceofdata,inrankorder,fromthesmallestvaluetothelargestvalue.Showsrange(minimumvaluetomaximumvalue)Mayhelpidentifyoutliers(unusualobservations)AgeofSurveyedCollegeStudentsDayStudents161717181818191920202122222527323842NightStudents181819192021232832334145BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-12Stem-and-LeafDisplayAsimplewaytoseehowthedataaredistributedandwhereconcentrationsofdataexistMETHOD:Separatethesorteddataseries

intoleadingdigits(thestems)and

thetrailingdigits(the

leaves)BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-13OrganizingNumericalData:

StemandLeafDisplayAstem-and-leafdisplayorganizesdataintogroups(calledstems)sothatthevalueswithineachgroup(theleaves)branchouttotherightoneachrow.

StemLeaf1232842AgeofCollegeStudents DayStudents NightStudentsStemLeaf1889920138323415AgeofSurveyedCollegeStudentsDayStudents161717181818191920202122222527323842NightStudents181819192021232832334145BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-14OrganizingNumericalData:

FrequencyDistributionThefrequencydistributionisasummarytableinwhichthedataarearrangedintonumericallyorderedclasses.

Youmustgiveattentiontoselectingtheappropriatenumber

ofclassgroupingsforthetable,determiningasuitablewidth

ofaclassgrouping,andestablishingtheboundaries

ofeachclassgroupingtoavoidoverlapping.Thenumberofclassesdependsonthenumberofvaluesinthedata.Withalargernumberofvalues,typicallytherearemoreclasses.Ingeneral,afrequencydistributionshouldhaveatleast5butnomorethan15classes.Todeterminethewidthofaclassinterval,youdividetherange(Highestvalue–Lowestvalue)ofthedatabythenumberofclassgroupingsdesired.BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-15OrganizingNumericalData:

FrequencyDistributionExampleExample:Amanufacturerofinsulationrandomlyselects20winterdaysandrecordsthedailyhightemperature24,35,17,21,24,37,26,46,58,30,32,13,12,38,41,43,44,27,53,27BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-16OrganizingNumericalData:

FrequencyDistributionExampleSortrawdatainascendingorder:

12,13,17,21,24,24,26,27,27,30,32,35,37,38,41,43,44,46,53,58Findrange:58-12=46Selectnumberofclasses:5(usuallybetween5and15)Computeclassinterval(width):10(46/5thenroundup)Determineclassboundaries(limits):Class1:10tolessthan20Class2:20tolessthan30Class3:30tolessthan40Class4:40tolessthan50Class5:50tolessthan60Computeclassmidpoints:15,25,35,45,55Countobservations&assigntoclassesBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-17OrganizingNumericalData:

FrequencyDistributionExample

ClassFrequency10butlessthan2030.151520butlessthan3060.303030butlessthan4050.252540butlessthan5040.202050butlessthan6020.1010

Total

201.00100RelativeFrequency

PercentageDatainorderedarray:12,13,17,21,24,24,26,27,27,30,32,35,37,38,41,43,44,46,53,58BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-18TabulatingNumericalData:

CumulativeFrequencyClass10butlessthan20 31531520butlessthan30 63094530butlessthan40 525147040butlessthan50420189050butlessthan60 210

20100Total 20100

PercentageCumulativePercentageDatainorderedarray:12,13,17,21,24,24,26,27,27,30,32,35,37,38,41,43,44,46,53,58FrequencyCumulativeFrequencyBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-19WhyUseaFrequencyDistribution?ItcondensestherawdataintoamoreusefulformItallowsforaquickvisualinterpretationofthedataItenablesthedeterminationofthemajorcharacteristicsofthedatasetincludingwherethedataareconcentrated/clusteredBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-20FrequencyDistributions:

SomeTipsDifferentclassboundariesmayprovidedifferentpicturesforthesamedata(especiallyforsmallerdatasets)ShiftsindataconcentrationmayshowupwhendifferentclassboundariesarechosenAsthesizeofthedatasetincreases,theimpactofalterationsintheselectionofclassboundariesisgreatlyreducedWhencomparingtwoormoregroupswithdifferentsamplesizes,youmustuseeitherarelativefrequencyorapercentagedistributionBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-21OrganizingNumericalData:

TheHistogramAverticalbarchartofthedatainafrequencydistributioniscalledahistogram.Inahistogramtherearenogapsbetweenadjacentbars.Theclassboundaries(orclassmidpoints)areshownonthehorizontalaxis.Theverticalaxisiseitherfrequency,relativefrequency,orpercentage.Theheightofthebarsrepresentthefrequency,relativefrequency,orpercentage.BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-22OrganizingNumericalData:

TheHistogram

ClassFrequency10butlessthan203.151520butlessthan306.303030butlessthan405.252540butlessthan504.202050butlessthan602.1010

Total

201.00100RelativeFrequency

Percentage(Inapercentagehistogramtheverticalaxiswouldbedefinedtoshowthepercentageofobservationsperclass)BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-23OrganizingNumericalData:

ThePolygonApercentagepolygonisformedbyhavingthemidpointofeachclassrepresentthedatainthatclassandthenconnectingthesequenceofmidpointsattheirrespectiveclasspercentages.Thecumulativepercentagepolygon,orogive,displaysthevariableofinterestalongtheXaxis,andthecumulativepercentagesalongtheYaxis.Usefulwhentherearetwoormoregroupstocompare.BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-24GraphingNumericalData:

TheFrequencyPolygonClassMidpointsClass10butlessthan2015320butlessthan3025630butlessthan4035540butlessthan5045450butlessthan60552FrequencyClassMidpoint(Inapercentagepolygontheverticalaxiswouldbedefinedtoshowthepercentageofobservationsperclass)BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-25GraphingCumulativeFrequencies:

TheOgive(Cumulative%Polygon)Class10butlessthan20 10 1520butlessthan30 204530butlessthan40 307040butlessthan50 409050butlessthan60 50100CumulativePercentageLowerclassboundaryLowerClassBoundary(Inanogivethepercentageoftheobservationslessthaneachlowerclassboundaryareplottedversusthelowerclassboundaries.BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-26CrossTabulationsUsedtostudypatternsthatmayexistbetweentwoormorecategoricalvariables.CrosstabulationscanbepresentedinContingencyTablesBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-27CrossTabulations:

TheContingencyTableAcross-classification(orcontingency)tablepresentstheresultsoftwocategoricalvariables.Thejointresponsesareclassifiedsothatthecategoriesofonevariablearelocatedintherowsandthecategoriesoftheothervariablearelocatedinthecolumns.Thecellistheintersectionoftherowandcolumnandthevalueinthecellrepresentsthedatacorrespondingtothatspecificpairingofrowandcolumncategories.

BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-28CrossTabulations:

TheContingencyTableImportanceofBrandNameMaleFemaleTotalMore450300750EqualorLess330034506750Total375037507500Asurveywasconductedtostudytheimportanceofbrandnametoconsumersascomparedtoafewyearsago.Theresults,classifiedbygender,wereasfollows:BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-29ScatterPlotsScatterplotsareusedfornumericaldataconsistingofpairedobservationstakenfromtwonumericalvariablesOnevariableismeasuredontheverticalaxisandtheothervariableismeasuredonthehorizontalaxisScatterplotsareusedtoexaminepossiblerelationshipsbetweentwonumericalvariablesBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-30ScatterPlotExampleVolumeperdayCostperday231252614029146331603816742170501885519560200BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-31ATimeSeriesPlotisusedtostudypatternsinthevaluesofanumericvariableovertimeTheTimeSeriesPlot:NumericvariableismeasuredontheverticalaxisandthetimeperiodismeasuredonthehorizontalaxisTimeSeriesPlotBusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-32TimeSeriesPlotExampleYearNumberofFranchises1996431997541998601999732000822001952002107200399200495BusinessStatistics:AFirstCourse,5e?2009Prentice-Hall,Inc.Chap2-33PrinciplesofExcellentGraphsThegraphshouldnotdistortthedata.Thegraphshouldnotcontainunnecessaryadornments(sometimesreferredtoas