华东理工大学多元统计分析与SPSS应用实验

1、华东理工大学 20132014 学年 第 二 学期多元统计分析与SPSS应用实验报告4班级 学号 姓名开课学院商学院任课教师任飞成绩实验内容：实验4回归分析方法相关分析熟悉Correlate功能AnalyzeCorrelateBivariate选用 Trends chap ter 9. sav 文件中的变量，将 consump, income, 放入对话框，作二个随机变量的相关分析。选用 Employee data. sav 文件中的变量，将 Current Salary, educ, salbegin, gender,.放入对话框，作二个随机变量的相关分析。回归分析熟悉Regression功

2、能AnalyzeRegressionLiner 选用Employee data. sav文件中的变量，将salary作为因变量(dependent variables)，其它的，如 educ, salbegin, gender,.作为自变量 (independent variables)，作多元线性回归分析。注：Method框选用EnterMethod 框选用 Stepwise实验要求：对实验内容4. 1的结果进行分析试讨论“Employee data”是否能构成一个回归问题以及对回归结果展开讨论。 教师评语:教师签名： 年 月日实验报告：4.11、打开 Trends chap ter 9.

3、sav， 按照顺序 AnalyzeCorrela teBivaria te, 将 consump, income, 放入 对话框，如图4.1.1所示。图4.1.1点击OK,得到结果如图4.1.2图4.1.2+ CorrelationsCorrelationsCONSUMPINCOMECONSUMP Pearson Correlation1-.744+=Sig. (2-tailed.).000N6969INCOMEPearson Correlation-744+1Sig. (2-tailed).000N6969*叫 Correlation is significant at the 0.01 l

4、evel (2-tailed).Pearson相关系数为-0.744，带有两个“*”，表明在显著性水平为0.01下两变量是显著相关的，且F检 验P值为0,拒绝总体中这两个变量相关系数为零的假设，由此可得consump和income呈现出显著的负 相关。2、打开 Employee data, sav，将 Current Salary, educ, salbegin, gender,prevexp jobtime.全部放 入对话框，按照顺序AnalyzefCorrelateBivariate,如图 4.1.3 所示。图4.1.3I Bivariate CorrelaiioriE点击OK,得到结果如

5、图4.1.4 图4.1.4* Cur iela LionsCorrolaitlonsCurrent SslaiyEducational Level (yrars)BeginuLng SalaryGenderFreviousEsperience (rncnthsMonths ：5ince HireCurrent SalaryFfearson Correlati-MiL GCJ-,:ct-.450,09034jl：. tailed).UOlicco.0011的4067N474474474474414474Educational LrelPearson Cori elation.661*L EE3-

6、 356*- 252047(years)(2 tailed)nm.-.nnn.nnn汗X47447447d474,474Beginning SalaryFarMn Correlation.SSDw.6331-.457.045-020Si& 2 tailed).000.OQQ.000.327.佃N47447447d471471GenderFfeareDH Correlation-&5D柑-iSEB*5*-.-1575L-165-0BSig. 2-taiLed)JjOD.000.ccc.000 LOSN474+74474474474174Previous EKpalenceFearsoiL Cxj

7、rrelatlon-037-.252.045-355*L0D3(nonths).034.nnn94SN4-J44744/4Montns sinoa HireFearson c-orrelatl-Mi.064.047-LEO-066.0091si：. j-:aLc?).0&7.tea.14UN474474474474474474冲& Correlation is slgnmcatit at the D.OL level 2-talled).Correction is significant at the 0.05 levEl ： 2-tailed).以 Current Salary 为例，Cur

8、rent Salary 和 Educational Level、Beginning Salary、Gender、Previous Experience 的 Pearson 相关系数分别为 0.661 (*)、0.880 (*)、-0.450 (*)、-0.097 (*)，表明在 显著性水平为 0.01 下 Current Salary 和 Educational Level、Beginning Salary、Gender 是显著相关 的,“Mon ths Since Hire ”与其余变量无显著相关性。上述说明当前工资和职工受教育年限和起薪是显著正相关，和性别是负相关，这里“0”表示男性，“1

9、” 表示女性，结果也符合实际，一般来说，同等情况下男性工资水平比女性工资水平要高。当前工资和工 作年限有一定的关系，但显著性与前三个变量相比要弱。4.21、打开 Employee data.sav，按照顺序 Analyze Regression Liner ，将 salary 选入 Dependent： 框中，其它 educ, salbegin, gender,prevexp,job ti me,.选入 I ndependen t(s)中，Met hod 选 Ent er, 如 图4.2.1单击 “Statistics”,选择 “Estimates”、“Model fit”、“Confiden

10、ce intervals”、“Descriptive”，如图4.1.2图4.2.1单击“OK”,得到结果如图4.1.3、图4.1.4、图4.1.5、图4.1.6、图4.1.7 图 4.2.3Descriptive StatisticsMeanStd.DeviationNCurrent Salary Educational Level (years)Beginning SalaryGenderFrevious Esrperience Cmontlis)Months since Hire$34419.5713.49J17.016.09.4695.8681.11$1ZO75.6612.885 $7,

11、870.638.499104.58610.061474474474474474474图 4.2.4CorrElationsCurrent SalaryEducationial Level (years)Beginning SalaryGenderPrevious Experience(months)Months since HirePearson Correia廿onOirrent Salary1.000.EEil.880-1511-0.084Educatlona Level(yeais).6611.000启舲-.356-252.047EegLntjlng Salaiy.S31.D00-457

12、.0J5-D3DGender-450-cS6-45?1.000-105-DBBFren/lous EHperlsnce (months)-.097 D45-165i.ooa.DD9MonthE since HLre.094.047-D20-.066Q的l.DDOSig. (1-tailed)Current Salary.om.00.000.on.3d.Educational Level (jearsl,D00000.etc.Lj3BeBinning Salacry.000 OCO.000.163财4Gender.000.no D00血|叶4Previous EKperience (months

13、).on.OCO旧.000.mMonths since Hire.炸 1F2.334讥.氓NOirrent Salary474474474474474474Educatlona Level earsj474474474怕474EegLntjlng Salaiy47417147447ilGender454们44?4474i?lPrevious Bsperience (months)474474474474474474Montlis siiic：G Hlts474474474474474474图 4.2.5Model SummaryModelRR SquareAdjusted R SquareSt

14、d, Error of the Estimate1.902s.814.812$7,410.457Predictors； (Constant)j Months since Hire, Previous Experience (months), Begimiing SaLaryr Gender, Educational Level (years)图 4.2.6AWOVA15ModelSum of SquaresdfMeanSquareFSig.1RegressionResidual TotaL1.L22E+113.570E+101.379E+U4732.244E+105491487540S.692

15、.000aa- Predictors： (Constant)j Months siace Hirej Previous Experience (months)j Beginning Salaryj 石巳nd巳匚 Educational L-etvel CyearsDependent Variable： Current Salary图 4.2.7CflpfficiF!ntsaModelTJtigtandardied CoefficientsstandardlaeCoefficientstJig955? C&tifidence nterval for BB土c, b?iorBetaLowerBou

16、ndjpi：er Bour.c1(Const mt)-125S0.0329474.74-3.612工|匚|-19379. D65-5722 LOOEducational Level (years)btd.uilIbb. D30.1003.559.uuu265.595dJUBeginning Salary1.723.D61価428.472 DOOL.B041J342(TRrdR-2222.917TQ2. me-D65-2.819.005-389.397-67B WPrevious Experlence (months)19.4363.5C3-.119-5,4 開.000sc. rc-12395M

17、onthE since Hire154.536H4. II-h.0914.53.Illlll87.558仞hlha Detpendent Variable Current Salary结果分析：图4.2.3是描述统计量的结果图 4.2. 4 是相关分析的结果，Current salary 分别和 Educational Level、Beginning salary、Gender、 Previous Experience、Mon ths Since Hire 的相关系数分别是 0.661、0.880、-0.450、-0.097、0.084, 可知 Current Salary 和 Educati

18、onal Level、Beginning salary、Gender 有较高的相关性。图4.2.5是模型摘要，分别给出相关系数R、判断系数R Square、调整判断系数Adjusted R Square等 图4.2.6是方差分析，由回归均方F统计量检验值P=0可知，整体回归方程显著。图4.2.7是偏回归系数，由t统计量检验值P0.05，各回归系数均显著，可得回归方程为y = 12550.032 + 593.031x +1.732x -2232.917x -19.436x +154.536x +&123452、打开 Employee data.sav，按照顺序 Analyze Regressio

19、n Liner ，将 salary 选入 Dependent： 框中，其它 educ, salbegin, gender,prevexp,job ti me,.选入 I ndependen t(s)中，Met hod 选 St epwise。 结果如图4.2.8、图4.2.9、图4.2.10、图4.2.11、图4.2.12图4.2.8Variables En tered/Remov edaModelVariablesEnteredVariablesRemovedMethod12345BeginningSalaryPreviousExperience (months)Months since H

20、ireEducationa l Level (years)GenderStepwise (Criteri a： Probabili ty-of-F-t o-enter = .100).Stepwise (Criteri a:Probabili ty-of-F-t o-enter = .100). Stepwise (Criteria:Probabili ty-of-F-t o-enter = .100). Stepwise (Criteria:Probabili ty-of-F-t o-enter = .100). Stepwise (Criteria:Probabili ty-of-F-t

21、o-enter = .100).a. Dependent Variable： Current Salary图 4.2.9Model Su mmaryModelRR SquareAdjusted R SquareStd. Error of the Estimate1.880a775-.774$& 115.3562 891b.793.793$7,776.6523.897c.804.803$7,586.1874.900d.810.809$7,465.1395.902e.814.812$7,410.457Predictors： (Constant), Beginning SalaryPredictor

22、s: (Constant), Beginning Salary, Previous Experience (months)Predictors: (Constant), Beginning Salary, Previous Experience (months), Months since HirePredictors: (Constant), Beginning Salary, Previous Experience (months), Months since Hire, Educational Level (years)Predictors: (Constant), Beginning

23、Salary, Previous Experience (months), Months since Hire, Educational Level (years), Gender图 4.2.10ANOVAfModelSum of SquaresdfMean SquareFSig1Regression 1.068E+1111.068E+111622.118.000aResidual:1109E+1047265858997Total:.379E+114732Regression 1.094E+1125.472E+10904.752.000bResidual:L848E+1047160476323

24、Total:.379E+114733Regression 1.109E+113(3L696E+10642.151.000cResidual:L705E+1047057550240Total:.379E+114734Regression 1.118E+114fL794E+10501.450.000dResidual:L614E+1046955728307Total1.379E+114735Regression 1.122E+115fL244E+10408.692.000eResidual:L570E+1046854914875Total1I.379E+11473Predictors: (Cons

25、tant), Beginning SalaryPredictors: (Constant), Beginning Salary, Previous Experience (months)Predictors: (Constant), Beginning Salary, Previous Experience (months), Months since HirePredictors: (Constant), Beginning Salary, Previous Experience (months), Months since Hire, Educational Level (years)Pr

26、edictors: (Constant), Beginning Salary, Previous Experience (months), Months since Hire, Educational Level (years), GenderDependent Variable: Current Salary图 4.2.11Coeff icien tsaModelUnstandardizedCoefficientsStandardize dCoefficientstSigBStd ErrorBeta1(Constant)1928.206888.6802.170.031Beginning Sa

27、lary1.909.047.88040.276.0002(Constant)3850.718900.6334.276.000Beginning Salary1.923.045.88642.283.000Previous Experience (months)-22.4453.422-.137-6.558.0003(Constant)-10266.6292959.838-3.469.001Beginning Salary1.927.044.88843.435.000Previous Experience (months)-22.5093.339-.138-6.742.000Months sinc

28、e Hire173.20334.677.1024.995.0004(Constant)-16149.6713255.470-4.961.000Beginning Salary1.768.059.81530.111.000Previous Experience (months)-17.3033.528-.106-4.904.000Months since Hire161.48634.246.0954.715.000Educational Level (years)669.914165.596.1134.045.0005(Constant)-12550.0323474.744-3.612.000B

29、eginning Salary1.723.061.79428.472.000Previous Experience (months)-19.4363.583-.119-5.424.000Months since Hire154.53634.085.0914.534.000Educational Level (years)593.031166.630.1003.559.000Gender-2232.917792.078-.065-2.819.005a. Dependent Variable： Current Salary图 4.2.12Exclu ded V ariables eModelBeta IntSig.PartialCorrelationCollinearityStat is ticsTolerance1Educational Level (years).172a6.356.000.281.599Gender-.061a-2.482.013-.114.791Previous Experience (months)-.137a-6.558.000-.289.998Months