管理统计作业

PAGEPAGE1管理统计作业题目：对某公司81名员工工资进行分析，分析其任职部门、性别、年龄与工资间的关系。序号姓名单位性别年龄工资1陈子元002520002李应杰002727503刘恩利202822504马韧203027505马建军003131586艾丽013132007李荣华103131008陆玉刚003237509任立玲0131312510许志强1031367811卞玉杰0132400012董小凤0132400013韩帅臣1033325014王庆平1033334515于铁华1033354316崔永善1033321317郑顺0034525018檀凤志2034450019李春华0034534320王玉才2034443521王福全0034458022耿俊山2034465323高书臣0035525024甘树才2035455025段玉茹0135375026张富荣2135303527董淑花0135375028刘国英2136302529王幸荣0136375030刘桂娟2136332231周丽荣0137450032高宝良2037525033张月萍2137477534刘廷祥1037466035陈金凤0138450036郭世友2038525037王淑芬2138477538孙建平1038450039高荣英0138450040曹彦贵2038525041刘子荣2138434042陈俊杰1038458043裴丽艳0139450044程相波2039552045徐丽芹2139477546吴桂杰1039450047李恩霞2139537548裴志军1039480049弭会兰2140537550吴东顺1040480051孙志霞2140537552刘作贵1040480053史秋芳2141537554刘占国1041490055韩双菊2142525056王琳2142534057张月霞2143549058张锦绣1143337059王玉霞1144345060王慧梅1144325061杜秀君1145298062孙玉霞1145286063毕凤芝1145300064赵洪玲1146453065宋志梅1146423566唐雅君1146452567崔淑菊1147425068牛瑞英1147397069张淑媛1147426570董艳敏1147452571吴晓景1148468072王秀芹1148478073张春艳1148492074郑英欣1148509075刘春梅1148462076陈静1149433077张翠英1149452578董健颖1149564079刘金玲1149496080朱文革1150492081曹淑华11504480绘制散点图及箱形图：进行相关分析： Correlations年龄工资年龄PearsonCorrelation1。435（*＊）Sig。(2—tailed).。000N8181工资PearsonCorrelation.435(*＊)1Sig。（2—tailed)。000.N8181＊＊Correlationissignificantatthe0。01level(2—tailed）。 Correlations年龄工资Kendall'stau_b年龄CorrelationCoefficient1.000.323(*＊)Sig.（2—tailed）..000N8181工资CorrelationCoefficient.323（＊*）1。000Sig。（2-tailed)。000.N8181Spearman’srho年龄CorrelationCoefficient1.000。422(＊*）Sig.（2—tailed).。000N8181工资CorrelationCoefficient。422(＊＊）1.000Sig.(2—tailed)。000.N8181*＊Correlationissignificantatthe0.01level(2—tailed).三、进行回归分析： ModelSummary(b)ModelRRSquareAdjustedRSquareStd。ErroroftheEstimate1。435（a）.189.179782。083aPredictors:（Constant）,年龄bDependentVariable：工资 ANOVA(b)ModelSumofSquaresdfMeanSquareFSig。1Regression11268013.041111268013.04118.422。000（a)Residual48320664.09479611653.976Total59588677。13680aPredictors:（Constant）,年龄bDependentVariable:工资 Coefficients（a）ModelUnstandardizedCoefficientsStandardizedCoefficientstSig。BStd.ErrorBeta1（Constant)1912.786555.0213.446.001年龄60。25314。038.4354.292.000aDependentVariable：工资四、进行方差分析（只考虑因子主效应）：第一种 TestsofBetween—SubjectsEffectsDependentVariable:工资SourceTypeIIISumofSquaresdfMeanSquareFSig.CorrectedModel4415182。358（a）31471727.4532.054.113Intercept1296671423。33811296671423。3381809。632.000性别305238。5491305238。549。426。516单位4131898.93422065949.4672.883。062Error55173494.77877716538。893Total1533423433.00081CorrectedTotal59588677。13680aRSquared=。074(AdjustedRSquared=.038） ParameterEstimatesDependentVariable:工资ParameterBStd。ErrortSig。95％ConfidenceIntervalLowerBoundUpperBoundIntercept4637.182190。52324.339。0004257.8025016.562［性别=0］—125。737192。647—.653.516—509.346257。873[性别=1]0（a）..。。。[单位=0］-602。345259。941—2.317.023-1119.953—84.738[单位=1］-384。963220。904-1.743。085-824.83954.912[单位=2]0(a)。。。。.aThisparameterissettozerobecauseitisredundant。 TestsofBetween—SubjectsEffectsDependentVariable:工资SourceTypeIIISumofSquaresdfMeanSquareFSig.Model1478249938。222（a)4369562484。556515.761。000性别305238。5491305238。549。426。516单位4131898.93422065949.4672.883.062Error55173494。77877716538。893Total1533423433。00081aRSquared=.964(AdjustedRSquared=。962) ParameterEstimatesDependentVariable：工资ParameterBStd.ErrortSig。95%ConfidenceIntervalLowerBoundUpperBound[性别=0]4511。445206.11821。888.0004101.0134921。878［性别=1］4637.182190。52324