spss和sas統(tǒng)計(jì)實(shí)驗(yàn)指導(dǎo)書(shū)-資料下載頁(yè)
2025-05-13 22:11本頁(yè)面
【正文】 變量值合員工滿(mǎn)意度數(shù)據(jù)Z1Z2Z3Z4Z5Z6Z7Z8滿(mǎn)意度66646250585612555505959535112250474945464620555950545269112055594856475011246254684646511236060565352511215252695857621123565557394446115505068464556255854605952511125535255576564122525653576351205665525162471122506359535548112063576066515612626564658504552212147505749504820205366535955451252561555861586112323596460525456262655607260556712626565268405155303059516156525612525605362554763272752515745555920205657575259552626685871685361130306053616056511272764567450595718186753605353511242456566767565212424534649435048191953576552675917176040715756581242454454449424612323『步驟1』在spss的數(shù)據(jù)編輯窗口中輸入上表所示的數(shù)據(jù)。員工滿(mǎn)意度設(shè)為因變量MY，“Analyze”菜單“Regression”中選擇Linear命令『步驟2』在彈出的菜單中所示的Linear Regression對(duì)話(huà)框中，從對(duì)話(huà)框左側(cè)的變量列表中選擇滿(mǎn)意度變量my，將其他變量添加到Dependent框中，表示該變量是因變量。單擊statistics按鈕將打開(kāi)linear regression：statistics對(duì)話(huà)框，用來(lái)選擇輸出那些統(tǒng)計(jì)量圖46Linear regression對(duì)話(huà)框『步驟3』 單擊OK按鈕，即可得到spss多元線(xiàn)性回歸分析的結(jié)果。結(jié)果和討論：（1） 輸出結(jié)果的第一個(gè)表格：Descriptive Statistics MeanStd. DeviationNMY36Z136Z236Z336Z436Z536Z636Z7.1866736Z836該表格中輸入了8個(gè)自變量和一個(gè)因變量的一般統(tǒng)計(jì)結(jié)果，包括平均值、方差和個(gè)案數(shù)N為36。（2） 輸出的結(jié)果文件中的第二個(gè)表格如下：Correlations MYZ1Z2Z3Z4Z5Z6Z7Z8Pearson CorrelationMY.413.118.326.427.245.486 Z1.413.186.386.315.043.213.304 Z2.118.186.241.373.045 Z3.326.386.217.200.454.270 Z4.427.315.241.217.353.322.234 Z5.043.373.200.353.330 Z6.245.213.045.454.322.330.172 Z7 Z8.486.304.270.234.172Sig. (1tailed)MY..006.246.026.005.330.075.037.001 Z1.006..139.010.031.402.106.096.036 Z2.246.139..485.078.012.397.230.200 Z3.026.010.485..102.121.003.495.055 Z4.005.031.078.102..017.028.000.084 Z5.330.402.012.121.017..025.352.491 Z6.075.106.397.003.028.025..170.157 Z7.037.096.230.495.000.352.170..421 Z8.001.036.200.055.084.491.157.421.NMY363636363636363636 Z1363636363636363636 Z2363636363636363636 Z3363636363636363636 Z4363636363636363636 Z5363636363636363636 Z6363636363636363636 Z7363636363636363636 Z8363636363636363636該表格列出了各個(gè)變量之間的相關(guān)性，從該表格可以看出自變量Z1和因變量MY之間的相關(guān)性很大。（3） 輸出文件的第三個(gè)表格如下：該表格輸出的是被引入或從回歸方程中被踢除的各變量。該部分說(shuō)明在對(duì)編號(hào)為1的模型進(jìn)行分析時(shí)所采用的方法是全部引入法Enter。因變量為MY。 Variables Entered/Removed(b)ModelVariables EnteredVariables RemovedMethod1z6, z2, z1, z4, z5, z3(a).Entera All requested variables entered.b Dependent Variable: MY（4） 輸出的結(jié)果文件第四個(gè)表格如下： Model Summary(b)ModelRR SquareAdjusted R SquareStd. Error of the Estimate1.607(a).368.238a Predictors: (Constant), z6, z2, z1, z4, z5, z3b Dependent Variable: MY該表格是常用的統(tǒng)計(jì)量。（5） 輸出的結(jié)果文件中第五個(gè)表格如下： ANOVA(b)Model Sum of SquaresdfMean SquareFSig.1Regression6.028(a)Residual29 Total35 a Predictors: (Constant), z6, z2, z1, z4, z5, z3b Dependent Variable: MY該表格是方差分析表。（6） 輸出的結(jié)果文件中第六個(gè)表格是回歸系數(shù)分析。 Coefficients(a)Model Unstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant) .570.573z1.128.114.191.273z2.062.093.111.669.509z3.087.081.192.290z4.186.082.385.031z5.100.070z6.053.095.099.560.580a Dependent Variable: MY其中，Unstandardized Coefficients 為非標(biāo)準(zhǔn)化系數(shù)，S談大阮的澤地Coefficients為標(biāo)準(zhǔn)化系數(shù)，t為回歸系數(shù)檢驗(yàn)統(tǒng)計(jì)量，Sig為伴隨概率值。從表格中可以看出該多元回歸方程為：其中Z6和Z7被剔除。（7） 輸出的結(jié)果文件中第七個(gè)表格如下： Residuals Statistics(a) MinimumMaximumMeanStd. DeviationNPredicted Value36Std. Predicted Value.00036Standard Error of Predicted Value.675.27636Adjusted Predicted Value36Residual.0000036Std. Residual.000.91036Stud. Residual36Deleted Residual36Stud. Deleted Residual.00236Mahal. Distance.87936Cook39。s Distance.000.400.042.09036Centered Leverage Value.025.422.167.08136a Dependent Variable: MY這個(gè)表格是殘差統(tǒng)計(jì)結(jié)果表。(8)輸出結(jié)果的第八部分為圖形，為因變量和每個(gè)自變量之間的關(guān)系點(diǎn)圖。練習(xí)題：某種水泥在凝固時(shí)放出的熱量（單位：卡/克）Y與水泥中下列4中化學(xué)成分的百分比有關(guān)： X1： X2： X3： X4：現(xiàn)測(cè)得13組數(shù)據(jù)，如下圖所示，要求建立熱量與水泥化學(xué)成分之間的經(jīng)驗(yàn)回歸關(guān)系式。xi1xi2xi3xi4yi7266601291552115682011318477526331155922371176131224425418222147426140233411669121068812思考：假如使用不同的多元回歸分析方法，將會(huì)有什么樣的不同。（分別用全回歸，逐步回歸法，向后法，向前法）6
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