0911回归分析例题SPSS求解过程

1、0911回归分析例题SPSS求解过程-05应数信管信计回归分析例题SPSS求解过程1、一元线性回归M 4.2.1 为研究家庭收人与余庭俟品支出的关系*随机抽取了 10 个家庭，得到表4 2.1的数ftL试抿竭这些数据：1）建立家庭食品支出对家庭收人的样本回归直线*2）预测当家庭收人为4 200元时家庭的乎均負品支出及其豊信度 为95%的预测区间. 4.2.1 家庭收入与品支出裁堀单位土酉元寂庭1234567910寂麼收人203033401513265543食品克出798H54810910SPSS求解过程:Uxititled 一 SPSS Dala Edit ox10 :站10孜庭收入I食品史L

2、h120 007 00230 009 00333.008.00A40.0011.00515.005.00613.004.00726.008.00838.0010.00935.009.001043.0010.005G C “LoXinar Classi x Uata Reduction ScaleHotip ar mm r a c T o t * Trme S eis 1 e s SurvivalMuitipla Rwpon&w Mi tjibcAnlyiiComplex SamplesReports Descriptive Statistics TablKUompar e MrwJ.on ji

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4、labla sutsred.b. Deper dent Vari m：食品支出Modol SummaryModelRR squareAdjusts d H squareSid. Error of ins Estimate1.951*904.892.73304自.Predictors (Co nsta nt),ANOVAbModelSum of Squares.dfMean SquarerSig.1Regf&S-iohRssidlual Total4C.S014 20044 90Q1 a o40.001S3775.550.OOC5a. Predictors. (ConstanOi 家庭收入D.

5、Dependent Van able： 支出CoafficiertPModelLJhstandanlizedClientsStandardsdCo eflic idsag.9代Con耐训阿血醐Bsw. ErrorBetaLower BounilUpper Bound1C血眦2.173.7201017.Oil.5123.833SfflU.202023.9518.692.000J 43256a Dependent临曲履破出判别：？0? 2.173 0.202x，且x与 y的线性相关系数为r=0.951，回归方程的F检验值为75.559，对应F值的显著性概率是0.0000.05,表示 线性回归方程具

6、有显著性，当对应F值的显著性概率0.05,表示回归方程不具 有显著性。每个系数的t检验值分别是3.017与8.692，对应的检验显著性概率分别为：0.017（0.05 ）和0.000（0.05），即否定H。，也就是线性假设是显著的元非线性回归钢4.3J出钢时所用的盛钢水的钢包由于钢水对耐火材料的侵 蚀,容积不断增大希望找出使用次数X与增大的容积Y之间的关系试 验数据列于表4.3.L使用次数K与增大的容积丫的试验敷揭234567896.428.209 589.509.70ID.009+939.9910111213141516y10.4910.5910,6010.8010.6010.9010.76

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8、缰|副AnalyzeGraphs Utili ties Window Help次数X2345容积丫6.428.209.589.50Reort sDescriptive StatisticsTablesCompare MeanskronereJ. Linear Mode丄Mixd MudtflsCarrel ate二 zi onU11.85942.10417 2597LinevarvarG789109.7010.009.939.9910.49101112131112131410.5910.6010 3010R0141510 90IggI in”r Classify Data Reduction

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11、epti ride nt MthPsq ckfF SigibOb1b2b3容积丫 QUA 容积Y CUB曰田 1Z 35.6 .ODO_S211142.84 XOC6.0927.7100 02904.1 IBS 1L7OB0 -,1534.046 Y111 00-,Observed QuHdrsrtc CulslcW-oa-aOjOO-z&0Q-/7.00-/9B.OO-f12111l46Bin 口其Hll113 1*1 16所以,二次式为：Y6.09270.7408 x 0.029x2三次式为：Y 4.118231.7068X 0.1534X20.0046x32、把Y与X的关系用双曲线拟合：

12、作双曲线变换：U1 ,Vy1X次数X |容积|V|Uvar|var|v126.421.5000.1558238.20 3330.1220349.58.2500.1040459.50.2000.1053569.70.1667.10316710.00.1420.1000789.93.1250.1007899.99.1110.100191010.49.1000.0953101110.59.0900.0944111210.60.0830.0943121310.80D 760.0926131410.60.0714.0943141510.90.0667.0917151610.76D 625.0926161

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14、010.4910.0000.09532.350410111210.5911.0000.09442.35991110.6012.0000.09432 36091213141510.8013.0000.09262.37951310.6014.0000.09432.36091410.9015.0000.09172.3888151610.7616.0000.09292.3758h容积丫5423 23353 Compute VariableIMumeri Exprecsort125678910111213953刁43105913 6J1383TatVanaole:1伽丫1415

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16、0539.701附103110.001429IOCO9.93.125010079.99.1111100110.49.100009531059.0909C9441060.0833CA431080.0769097610.60 .071094310.90.0TO7C91710.76.0626C929右恕丫 Iuti次盼4 vi ui1314Z326262715169109 卫 iT1 cf ：尸“e |ndaelewi幼JMethM* |CrMerAeatCieeCependerrQ iuPaiicHeb1517filvzbonVaiiKle;J2029on RegressionVariables

17、Entere(l/RemovedbModelVariables EnteredVariablesRemovedM ethod1WEntera All requested variables entered.b Dependent Variable: UModel SummaryModelRR SquareAdjusted R SquareStd. Error of the Estimate1.968*.938.933.0042568a. Predictors: (Constant), VANOVAbModelSum of SquaresdfMean SquareFSig.1Regression

18、Residual Total.004.000.00411314.004.000196.227.000aa. Predictors: (Constant). Vb. Dependent Variable: UCocflicieiW1/loJeunslandaTdlLzedCoefficientsSlanJardlzEdCoefflciehtetClQ95% Confidence InrteralforDeStd E-io9e:aLower BoundUp 皿汨 LintJjonetantjL紙JffOJM.51 I.OUQJ78JOBSV131J0013.ytia11.00a.000111.15

19、1a. ependentVarable: U判别：U 0.082 0.131V , U -,V- , V与U的相关系数为R=0.968,回归y x方程系数的F检验值为196.227，对应F值的显著性概率是0.000 (0.05),表 示线性回归方程具有显著性，每个系数的t检验值分别是440514与14.008，对应的检验显著性概率分别为：0.000 (0.05 )和0.000 (1屮|9；|6=Jhi|0|.JUl1-Factions:flBS(nuTK4)i value.)hR$IH(nunig*mWU 閉(ruinexti CDFNOFM 刖祗: CDF.BERNOULUMResetH?l

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21、 1 o? 1incependntft)Z1IE niwRegressionVariables EirteredReinoved6ModelVariables E nteredVariables RemovedMetnod1vaEntera. All requested variables entered.D. Dependent Variable: U1Model SummaryModelRR CquorcAdjusted R GquarcStd. Error of the Eotimotc147沪.959.956.0289852Q Predictors: (Constant), VANOV

22、AbMndftlSum of SquarpsrtfMpan SquareFSig.1RegressionResidual Total.255 .011.26611314.255 .031303.190000aa. Predictors: (Constant). V b Dependent Variable: U1Ccefficieiitsrioceii irKtfindardiid CDifficientsCoeffic-iemtstSiu95% ConffidencB IntervalforlDDStd. EinarBetaLower B 3rcUtiper Boumd(Canislsin2

23、.15E.1315.221joac2.4312 IBS-1.111-.679-17.12.DOC-1.246-.973a. DependritVnitle- IJ1判别：U1 2.458 1.111V , U1 In y,V 1/x , V 与U1 的相关系数为 R=0.979, 回归方程的F检验值为303.190，对应F值的显著性概率是0.000 (0.05),表 示线性回归方程具有显著性 ，每个系数的t检验值分别是195.221与-17.412， 对应的检验显著性概率分别为：0.000 (0.05 )和0.000( 0.05)，即否定H。也就是线性假设是显著的。三、多元线性回归例4,4.1

24、在平炉炼钢中由于矿石与炉气的筑化作用*铁水的总含 碳量在不断降低一炉钢在冶炼初期总的左碳量V与所加的两种矿石的 BX,、心及熔化时间位出min)有关”经实测某号平炉的49组数 撮如表4*4* 所示求Y对Xx , X2 ,X3的线性冋必方稈*检验冋归方稈 和冋归系数的显著性如有不显著的变竜，请剔除之并求别除不皿袴的变 量之后的回归方程.K 4.4,1CBKiHlWKM址号工,yCO曲7Jf 1 (*- j(HI Ejl 5 mirii)y12f304.330 226Vtg2.亍“在科274027123-51毎.6J E 4354j4.4H3 061 4 11 S N41 233JS . 541

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30、g. (ConctCinO.X3.X1.X2 o DoponconiVariabio:YCoefncieirts-ModelUmshnclanliiedComciartsSlaridardiiedl Cocflieiointe1El.bi% Conrklsiflco intfiivslfofBDGtl ErrcrDetaLcwerDoindUpper DoLrid；CLdlijnli895BtKSD342ad.Mfl2fi3FX11B1.61726&3Ullmg.282X2109.D57-6722876QOb185X3osa.01134DIODImsoflr去 D epend ent Vstlaale y 0.695 0.16篦0.10&20.03&3 , Xi,X2,X3与y的复相关系为R=0.582，回归方程的F检验值为7.702，对应F值的显著性概率是0.000(0.05 )、0.011(0.05 )、0.006(0.05)和 0.001 ( 0.01),剔除X1,结