离散选择模型logit模型实例stata分析

离散选择模型logit模型实例stata分析ContentsIntroductionSPDatasetinformationModelingscenariossettingDatamodificationModelingestimatedresultsModelingcomparisonConclusionIntroductionThispaperdevelopedadisaggregatedlogisticsdemandmodelsusingdiscretechoiceanalysismethod.Datausedis2008-SPdatafromasurvey.Statawasemployedfortheestimationoflogitmodels.SPDatasetinformationStatedpointed:2007Analysiscommodity:steelAnalysisrange:30people(row600-1500)Dependentvariable:choiceIndependentvariables:cost[log(#/10^5)] time[hour] LOS[#/10]ModelingScenariosSettingModelingscenariosMarketSegment?YNMODEL1-1-Distance5/63variables(cost/time/LOS)MODEL2-1-Distance5/62variables(cost/time)MODEL3-1-Distance5/62variables(cost/LOS)MODEL4-1-Distance5/62variables(time/LOS)MODEL1-23variables(cost/time/LOS)MODEL2-22variables(cost/time)MODEL3-22variables(cost/LOS)MODEL4-22variables(time/LOS)DataModificationWemodifyrow-datatoremoveunreasonabledataset-Suchasthechoiceofthenot-dominantalternativeModel1has1unreasonabledatasets(inalldatasets)Model2has31unreasonabledatasets(inalldatasets)Model3has8unreasonabledatasets(inalldatasets)Model4has85unreasonabledatasets(inalldatasets)Model4isignoredidmodedistanceqtypecosttimeloschoicelogcost5los2223rail51210000146000.7419376223truck5128000066011.0296196223rail5102100001910000.74193710223truck51028000076011.0296196Datareorganizeexleiddistancequestiontyperailtruckchoicecosttimeloscosttimelos22351210000146028000066022235226000014803500007602BasicdataDataModificationModelingEstimatedResults(DIST5)Modeldistance51-1-50.28992-1-50.28843-1-50.1042ModelingEstimatedResults(DIST6)Modeldistance61-1-60.25882-1-60.25393-1-60.0704ModelingEstimatedResults(DIST5&6)ModelDistance5&61-20.25392-20.25213-20.0838ModelingComparisonConclusionThebestmodelisModel1-1Statisticalresultsoftheestimationindicatethatmarketsegmentationmakesthemodelmorecomprehensive.Themorevariablesmodelhas,themorecomprehe