华理SPSS实验四.docx

华东理工大学20132014 学年 第 一 学期 应用统计学 实验报告4实验内容：回归分析方法 1.相关分析 熟悉Correlate功能2.回归分析 熟悉 Regression功能 实验要求：1.选取文件Employee data. sav 中合适的变量，进行相关分析。 2.选用Employee data. sav 文件中的变量,将salary作为因变量(dependent variables)， 以educ, salbegin, gender,jobtime, prevexp, minority作为自变量(independent variables),作多元线性回归分析：Method框选用Enter , Method框选用Stepwise。 对回归结果展开讨论。 找出对salary影响显著的变量，并比较各因素的相对作用程度。3. 分别写出男性、女性的回归方程。 若要将变量jobcat引入回归方程，如何设置其取值？教师评语： 教师签名： 年 月 日实验报告：1. 选取文件Employee data. sav 中合适的变量，进行相关分析。打开Employee data.sav,选择Analyze-CorrelateBivariate，将education level, current salary, beginning salary, previous experience分别选入Variables”中，点击OK。结果如下：从图中看出，education level和current salary, beginning salary, previous experience的相关系数分别为0.661,0.633,-0.252，在这些数据的右边都有两个星号，表示在0.01的显著水平下，是显著相关的。还有一些数据右边是一个星号，表示在0.05的显著水平下，是显著相关的。2.选用Employee data. sav 文件中的变量,将salary作为因变量(dependent variables)， 以educ, salbegin, gender,jobtime, prevexp, minority作为自变量(independent variables),作多元线性回归分析：Method框选用Enter , Method框选用Stepwise 使用Enter方法：2.1描述统计量，显示均值，标准差，例数Descriptive StatisticsMeanStd. DeviationNCurrent Salary$34,419.57$17,075.661474Gender.46.499474Educational Level (years)13.492.885474Employment Category1.41.773474Beginning Salary$17,016.09$7,870.638474Months since Hire81.1110.061474Previous Experience (months)95.86104.586474Minority Classification.22.4144742.2相关分析从以下图表中可以看出gender, educ, salbegin, jobtime, prevexp, minority和current salary的相关系数分别是-0.450,0.661,0.880,0.084-0.097,-0.177，单尾单测检验分别为P=0.000,0.000,0.000,0.034,0.017,0.000，相关度较高。CorrelationsCurrent SalaryGenderEducational Level (years)Beginning SalaryMonths since HirePrevious Experience (months)Minority ClassificationPearson CorrelationCurrent Salary1.000-.450.661.880.084-.097-.177Gender-.4501.000-.356-.457-.066-.165-.076Educational Level (years).661-.3561.000.633.047-.252-.133Beginning Salary.880-.457.6331.000-.020.045-.158Months since Hire.084-.066.047-.0201.000.003.050Previous Experience (months)-.097-.165-.252.045.0031.000.145Minority Classification-.177-.076-.133-.158.050.1451.000Sig. (1-tailed)Current Salary.000.000.000.034.017.000Gender.000.000.000.074.000.050Educational Level (years).000.000.000.152.000.002Beginning Salary.000.000.000.334.163.000Months since Hire.034.074.152.334.474.141Previous Experience (months).017.000.000.163.474.001Minority Classification.000.050.002.000.141.001.NCurrent Salary474474474474474474474Gender474474474474474474474Educational Level (years)474474474474474474474Beginning Salary474474474474474474474Months since Hire474474474474474474474Previous Experience (months)474474474474474474474Minority Classification4744744744744744744742.3采用Enter方式Variables Entered/RemovedbModelVariables EnteredVariables RemovedMethod1Minority Classification, Months since Hire, Gender, Previous Experience (months), Employment Category, Educational Level (years), Beginning Salarya.Entera. All requested variables entered.b. Dependent Variable: Current Salary2.4模型摘要Model SummarybModelRR SquareAdjusted R SquareStd. Error of the Estimate1.903a.815.812$7,397.678a. Predictors: (Constant), Minority Classification, Months since Hire, Gender, Previous Experience (months), Educational Level (years), Beginning Salaryb. Dependent Variable: Current Salary2.5方差分析ANOVAbModelSum of SquaresdfMean SquareFSig.1Regression1.124E1161.873E10342.191.000aResidual2.556E104675.473E7Total1.379E11473a. Predictors: (Constant), Minority Classification, Months since Hire, Gender, Previous Experience (months), Educational Level (years), Beginning Salaryb. Dependent Variable: Current Salary2.6回归分析CoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.95% Confidence Interval for BBStd. ErrorBetaLower BoundUpper Bound1(Constant)-12059.7613481.961-3.463.001-18902.011-5217.510Gender-2418.777799.012-.071-3.027.003-3988.881-848.674Educational Level (years)589.259166.359.1003.542.000262.354916.165Beginning Salary1.707.061.78727.868.0001.5861.827Months since Hire156.51134.048.0924.597.00089.604223.417Previous Experience (months)-18.7643.601-.115-5.210.000-25.841-11.687Minority Classification-1377.447851.277-.033-1.618.106-3050.254295.360a. Dependent Variable: Current Salary由上表可得; gender educ, salbegin, jobtime, prevexp 的P值分别为0.001,0.003,0.000,0.000,0.000,0.000.认为回归系数都显著有意义，而 minority的P值为0.106，不具显著性。求得回归方程为：y=-12059.761-2418.777x1+589.259x2+1.707x3+156.511x4-18.764x5+ 使用Stepwise方法：2.11引入或剔除的变量Variables Entered/RemovedaModelVariables EnteredVariables RemovedMethod1Beginning Salary.Stepwise (Criteria: Probability-of-F-to-enter = .110).2Previous Experience (months).Stepwise (Criteria: Probability-of-F-to-enter = .110).3Months since Hire.Stepwise (Criteria: Probability-of-F-to-enter = .110).4Educational Level (years).Stepwise (Criteria: Probability-of-F-to-enter = .110).5Gender.Stepwise (Criteria: Probability-of-F-to-enter = .110).a. Dependent Variable: Current Salary2.12模型摘要Model SummaryfModelRR SquareAdjusted R SquareStd. Error of the Estimate1.880a.775.774$8,115.3562.891b.793.793$7,776.6523.897c.804.803$7,586.1874.900d.810.809$7,465.1395.902e.814.812$7,410.457a. Predictors: (Constant), Beginning Salaryb. Predictors: (Constant), Beginning Salary, Previous Experience (months)c. Predictors: (Constant), Beginning Salary, Previous Experience (months), Months since Hired. Predictors: (Constant), Beginning Salary, Previous Experience (months), Months since Hire, Educational Level (years)e. Predictors: (Constant), Beginning Salary, Previous Experience (months), Months since Hire, Educational Level (years), Genderf. Dependent Variable: Current Salary2.13方差分析ANOVAfModelSum of SquaresdfMean SquareFSig.1Regression1.068E1111.068E111.622E3.000aResidual3.109E104726.586E7Total1.379E114732Regression1.094E1125.472E10904.752.000bResidual2.848E104716.048E7Total1.379E114733Regression1.109E1133.696E10642.151.000cResidual2.705E104705.755E7Total1.379E114734Regression1.118E1142.794E10501.450.000dResidual2.614E104695.573E7Total1.379E114735Regression1.122E1152.244E10408.692.000eResidual2.570E104685.491E7Total1.379E11473a. Predictors: (Constant), Beginning Salaryb. Predictors: (Constant), Beginning Salary, Previous Experience (months)c. Predictors: (Constant), Beginning Salary, Previous Experience (months), Months since Hired. Predictors: (Constant), Beginning Salary, Previous Experience (months), Months since Hire, Educational Level (years)e. Predictors: (Constant), Beginning Salary, Previous Experience (months), Months since Hire, Educational Level (years), Genderf. Dependent Variable: Current Salary2.14回归系数CoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.95% Confidence Interval for BBStd. ErrorBetaLower BoundUpper Bound1(Constant)1928.206888.6802.170.031181.9473674.464Beginning Salary1.909.047.88040.276.0001.8162.0032(Constant)3850.718900.6334.276.0002080.9625620.473Beginning Salary1.923.045.88642.283.0001.8342.012Previous Experience (months)-22.4453.422-.137-6.558.000-29.170-15.7203(Constant)-10266.6292959.838-3.469.001-16082.782-4450.475Beginning Salary1.927.044.88843.435.0001.8402.015Previous Experience (months)-22.5093.339-.138-6.742.000-29.070-15.949Months since Hire173.20334.677.1024.995.000105.062241.3444(Constant)-16149.6713255.470-4.961.000-22546.785-9752.558Beginning Salary1.768.059.81530.111.0001.6531.884Previous Experience (months)-17.3033.528-.106-4.904.000-24.237-10.370Months since Hire161.48634.246.0954.715.00094.190228.781Educational Level (years)669.914165.596.1134.045.000344.511995.3165(Constant)-12550.0323474.744-3.612.000-19378.065-5722.000Beginning Salary1.723.061.79428.472.0001.6041.842Previous Experience (months)-19.4363.583-.119-5.424.000-26.478-12.395Months since Hire154.53634.085.0914.534.00087.558221.515Educational Level (years)593.031166.630.1003.559.000265.595920.467Gender-2232.917792.078-.065-2.819.005-3789.387-676.447a. Dependent Variable: Current Salary2.15模型外变量Excluded VariablesfModelBeta IntSig.Partial CorrelationCollinearity StatisticsTolerance1Gender-.061a-2.482.013-.114.791Educational Level (years).172a6.356.000.281.599Months since Hire.102a4.750.000.2141.000Previous Experience (months)-.137a-6.558.000-.289.998Minority Classification-.040a-1.794.073-.082.9752Gender-.088b-3.740.000-.170.771Educational Level (years).124b4.363.000.197.520Months since Hire.102b4.995.000.2251.000Minority Classification-.019b-.869.385-.040.9523Gender-.079c-3.406.001-.155.765Educational Level (years).113c4.045.000.184.516Minority Classification-.024c-1.127.260-.052.9504Gender-.065d-2.819.005-.129.744Minority Classification-.024d-1.185.237-.055.9505Minority Classification-.033e-1.618.106-.075.930a. Predictors in the Model: (Constant), Beginning Salaryb. Predictors in the Model: (Constant), Beginning Salary, Previous Experience (months)c. Predictors in the Model: (Constant), Beginning Salary, Previous Experience (months), Months since Hired. Predictors in the Model: (Constant), Beginning Salary, Previous Experience (months), Months since Hire, Educational Level (years)e. Predictors in the Model: (Constant), Beginning Salary, Previous Experience (months), Months since Hire, Educational Level (years), Genderf. Dependent Variable: Current Salary由上表可知：逐步回归法首先引入beginning salary,建立模型1，接着引入previous experience, job time, educational level, gender, 等变量，引入剔除，最后得到模型5，由回归系数的P值检验可得，在0.1的显著水平下，previous experience, job time, educational level, gender,的系数都显著有意义，所以模型5的回归方程为y=-12550.032+1.723x1-19.436x2+154.536x3+593.031x4-2232.917x5+.3. 分别写出男性、女性的回归方程。 若要将变量jobcat引入回归方程，如何设置其取值？引入0-1变量，设Di=1表示性别为男 Di=0表示性别为女，由上表可得回归方程为：y=-12550.032+1.723x1-19.436x2+154.536x3+593.031x4-2232.917Di引入jobcat后CoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant)1928.206888.6802.170.031Beginning Salary1.909.047.88040.276.0002(Constant)1036.931832.0511.246.213Beginning Salary1.469.067.67721.873.000Employment Category5947.000683.430.2698.702.0003(Consta