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LLLCh0:FirstThingsFirstandCh1:DefiningandcollectingdataLearningObjectives•WhatisStatistics?什么是统计学•BasicconceptsinStatistics统计学中的基本概念–Data,variable,population,sample,parameter,statistic,etc…–数据、变量、人口样本统计量、参数，等等……•Data/variabletypes数据/变量类型•Howtocollectdata如何收集数据•Thedifferentwaystocollectasample收集样本的不同方法•Thetypesofsurveyerrors调查误差的类型1LLLWhatisStatistics?•Statisticsreferstomethodsthathelptransformdataintousefulinformationfordecisionmakers.•统计指的是帮助决策者将数据转化为有用信息的方法。•Statisticsisawayofthinkingthatcanleadtobetterdecisions.•统计是一种可以带来更好决策的思维方式。2LLLWhyStatistics?•Intoday’sdigitalworldeverincreasingamountsofdataaregathered,stored,reportedon,andavailableforfurtherstudy.–Businessinformationsystems•在当今的数字世界中，越来越多的数据被收集、存储、报告，并可供进一步研究。-商业信息系统•Youheartheworddataeverywhere.•你到处都听到“数据”这个词。•Dataarefactsabouttheworldandareconstantlyreportedbyaneverincreasingnumberofsources.•数据是关于世界的事实，并且不断地被越来越多的来源所报道。3LLLToProperlyApplyStatisticsYouShouldFollowAFrameworkToMinimizePossibleErrors为了正确地应用统计数据，您应该遵循一个框架，以尽量减少可能出现的错误。InthiscoursewewilluseDCOVA–Definethedatayouwanttostudyinordertosolveaproblemormeetanobjective–Collectthedatafromappropriatesources–Organizethedatacollectedbydevelopingtables–Visualizethedatabydevelopingcharts–Analyzethedatacollectedtoreachconclusionsandpresentresults在这个过程中我们将使用DCOVA–-定义你想研究的数据，以解决问题或达到一个目标。–-从适当的来源收集数据–-组织开发表收集的数据–-通过开发图表来可视化数据–-分析收集到的数据，得出结论并给出结果4LLLUsingTheDCOVAFrameworkHelpsYouToApplyStatisticsTo:使用DCOVA框架帮助你申请统计：•Summarize&visualizebusinessdata•总结和可视化业务数据•Reachconclusionsfromthosedata•从这些数据中得出结论•Makereliableforecastsaboutbusinessactivities•对业务活动作出可靠的预测•Improvebusinessprocesses•改进业务流程5LLLBusinessAnalytics:TheChangingFaceOfStatistics商业分析：统计数据的变化•Useinformationsystemsmethodstocollectandprocessdatasetsofallsizes,includingverylargedatasetsthatwouldotherwisebehardtoexamineefficiently.•使用信息系统方法收集和处理各种大小的数据集，包括非常大的数据集，否则很难有效地检查这些数据集。•Usestatisticalmethodstoanalyzeandexploredatatouncoverunforeseenrelationships.•使用统计方法分析和探索数据，以发现不可预见的关系。•Usemanagementsciencemethodstodevelopoptimizationmodelsthatimpactanorganization’sstrategy,planning,andoperations.•使用管理科学方法开发影响组织战略、规划和运作的优化模型。•Thegrowthof“BigData”spurstheuseofbusinessanalytics•“大数据”的增长刺激了商业分析的应用•“Bigdata”orverylargedatasetsarearisingbecauseoftheautomaticcollectionofhighvolumesofdataatveryfastrates.•“大数据”或非常大的数据集的出现，是因为以非常快的速率自动收集大量数据。6LLLDataVocabulary数据的词汇–Data:measurementsthatarecollected,recorded,andsummarizedforpresentation,analysis,andinterpretation––数据：收集、记录和总结用于陈述、分析和解释的测量–Variable:characteristicoftheelementswhosevaluesmaydifferfromelementtoelementandisofinteresttothedatacollector–变量：元素的特征，其值可能不同于元素到元素，并且对数据收集器感兴趣。–Element:anentityorobjectonwhichdataarecollected.Alsocalledcase,subject,individual,item-–元素：收集数据的实体或对象。也称案件、主体、个人、项目–Observation:measurementofavariableonasingleelement–-观察：单个元素上变量的测量7LLLDataVocabularyCaseNameAgeIncomePositionGender1Frieda45$67,100PersonneldirectorF2Stefan3256,500OperationsmanagerM3Barbara5588,200MarketingVPF4Donna2759,000StatisticianM5Larry4636,000SecurityguardF6Alicia5268,500ComptrollerM7Alex6592,500ChiefexecutiveM8Jaime5071,200PublicrelationsF5variables8subjects/elements/individuals/items40observations8LLLDataVocabulary•TypesofVariables变量类型–Qualitative:labelsornamesforacharacteristic(position,gender,name)–-定性：特征的标签或名称（位置，性别，名字）–Quantitative:measurementofamountorquantity–-定量：量或量的测量•Discrete(counting)(#offamilynumbers):limitedvaluesinarange•离散（计数）（#家属）：在一个有限的范围值•Continuousvariable(measuring)(age,income):anyvalueinarange•连续变量（测量）（年龄，收入）：某一范围内的任何值9LLLDataVocabularyVariabletypesQualitative(Nominal,categorical)Quantitative(Numerical)DiscreteContinuousWords?Integers?10定性的（名义的，明确的变量类型定量（数值）分离的，不相关联的连续的整数？语言?AmountofInformation1.Nominallevel2.Ordinallevel3.Intervallevel4.RatiolevelNoorderordered/rankede.g.EyecolorRatingofaprofessorAbsolutezeroDifferenceismeaningfulRatioisalsomeaningfulSalaryLevels/ScalesofmeasurementNotruezeroDifferenceismeaningfulRatioisnotmeaningfulTemperatureFourLevelsofMeasurement11测量的四个层次信息量水平/测量尺度1。标称等级2。顺序层次3.区间水平4.率水平LLLFourLevelsofMeasurement•Qualitativedata:NominalandOrdinallevels–Nominalscale/level:Valuesrepresentcategoryorgroupmembershipofelements.Onlyshowdifference).Noorderimplied.•定性数据：名词和序数级-名义量表/级别：值表示元素的类别或组成员关系。仅表现出差异）。无订单暗示。–Ordinalscale/level:valuesconveylessthan,equalto,andgreaterthanrelationshipsamongelements,i.e.therelativeranksoftheelementswithrespecttotheirvaluesforthevariableinquestion(onebetterthananother?)(ratingsofcustomerservice:good,average,poor)–-序数量/等级：值传递小于，等于，大于元素之间的关系，即相对于变量的值的元素的相对秩（一个比另一个更好？）（客户服务等级：好的，一般的，差的）12LLLFourLevelsofMeasurement•Quantitativedata:IntervalandRatioScales–Intervalscale/level:thedifferencebetweenmeasurementsisameaningfulquantitybutdoesnotinvolveatruezeropoint•Fahrenheittemperature:differencebetween68-70isthesameas70-72.0degreedoesnotmeannotemperature.•定量数据：区间和比率标度-间隔刻度/水平：测量之间的差异是有意义的数量，但不包括真正的零点。•华氏温度之间的差别是：6870-72相同。0度并不意味着没有温度。–Ratioscale:valuescantakeonanaturalorabsolutezeroandratioismeaningful•Salary:0meansnoincome.•40000istwiceasmuchas20000.80000istwic