多元統(tǒng)計分析 實驗報告,計算協(xié)方差矩陣,相關(guān)矩陣,SAS

一）院系：數(shù)學(xué)與統(tǒng)計學(xué)學(xué)院專業(yè)：——統(tǒng)計學(xué)年級：2009級課程名稱：統(tǒng)計分析學(xué)號：姓名：指導(dǎo)教師：2012年4月28日一）實驗名稱編程計算樣本協(xié)方差矩陣和相關(guān)系數(shù)矩陣

多元方差分析MANOVA。二）實驗?zāi)康?.學(xué)習(xí)編制sas程序計算樣本協(xié)方差矩陣和相關(guān)系數(shù)矩陣2.對數(shù)據(jù)進行多元方差分析。三）實驗數(shù)據(jù)第一題：x1x2x3x4x5x6x74489.4744.60911.37621781824075.0745.31310.07621851854485.8454.2978.65451561684268.1559.5718.17401661723889.0249.8749.22551781804777.4544.81111.63581761764075.9845.68111.95701761804381.1949.09110.85641621704481.4239.44213.08631741763881.8760.0558.63481701864473.0350.54110.13451681684587.6637.38814.03561861924566.4544.75411.12511761764779.1547.27310.6471621645483.1251.85510.33501661704981.4249.1568.95441801855169.6340.83610.95571681725177.9146.67210481621684891.6346.77410.25481621644973.3750.38810.08761681685773.3739.40712.63581741765479.3846.0811.17621561655276.3245.4419.63481641665070.8754.6258.92481461555167.2545.11811.08481721725491.6339.20312.88441681725173.7145.7910.47591861885759.0850.5459.93491481554976.3248.6739.4561861884861.2447.9211.5521701765282.7847.46710.553170172第二題：kindx1x2x3x4

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四）實驗內(nèi)容1.打開SAS軟件并導(dǎo)入數(shù)據(jù)；2.編制程序計算樣本協(xié)方差矩陣和相關(guān)系數(shù)矩陣編制sas程序?qū)?shù)據(jù)進行多元方差分析；根據(jù)實驗結(jié)果解決問題，并撰寫實驗報告；（五）實驗體會（結(jié)論、評價與建議等）第一題：程序如下：proccorrdata=sasuser.shancov;proccorrdata=sasuser.shannosimplecov;withx3x4;partialx1x2;run;結(jié)果如下：（1）協(xié)方差矩陣117變星：::<1SAS丟統(tǒng)CORR過程:：＜E：::＜708:15Friday,April28,2000x2x3:：＜4::＜5協(xié)方差矩陣」自由度=30:d:：＜2:：＜3＞：:4:：＜5:：＜6:：＜7xl:：＜127.1591398-10.1364946-8.45626451.3647097-6.1193548-18.0516129-20.6752688:：＜2::＜2-10.136494669.3650389-7.22105081.65825471.568204315.498655919.0337204:：＜3::＜3-8.4562645-7.221050828.3793848-6.3725471-15.3064183-21.7352376-11.5574785＞：:4:：＜41.36470971.6582547-6.37254711.92491784.60930114.46124732.8747634x5-6.11935481.5682043-15.30641834.609301168.797849527.038709719.5731183xt：xE：-18.051612915.4986559-21.73523764.461247327.0387097105.103225887.3505376x7:：＜7-20.675268819.0337204-11.55747852.874763419.573118387.350537683.98064522）相關(guān)系數(shù)矩陣Pearson相關(guān)系數(shù),Nz31

當(dāng)HO:Rho=0時，Prob＞|r|x1x2x3x4x5x6x7x11.00000-0.23354-0.304590.10875-0.14157-0.33707-0.43292x10.20610.09570.30920.44750.06300.0150x2-0.233541.00000-0.162750.143510.022700.131520.24930x20.20610.38170.44120.90350.32040.1761x3-0.30459-0.162751.00000-0.86219-0.34641-0.31797-0.23674x30.09570.3817＜.00010.05630.02660.1997x40.108750.14351-0.882191.000000.400540.313850.22610x40.30920.4412＜.00010.02560.00500.2213x5-0.141570.02270-0.346410.400541.000000.317970.25750x50.44750.90350.05630.02560.08130.1620x6-0.337070.10152-0.397970.313650.317971.000000.92975x60.06300.32040.02860.08580.0813＜.0001x7-0.432920.24938-0.236740.226100.257500.929751.00000x70.01500.17610.19970.22130.1620＜.0001第二題：程序如下：procanovadata=sasuser.huang;classkind;modelx1-x4=kind;manovah=kind;run;結(jié)果如下：（1）分組水平信息TheANOVAProcedureClassLevelInformaticinClassLeveIsValuesTheANOVAProcedureClassLevelInformaticinClassLeveIsValueskindNumberofobservations602）x1、x2、x3、x4的方差分析TheANOVAProcedureDependentVariable：xlxlSumofSourceDFSquaresMeanSquareFValuePr>FModel25221.300002610.650003.380.0411Error5?44069.55000773.15000CorrectedTotal5949290.85000R-SquareCoeffVarRootMSExlMean0.10592832.3508?27.8055?85.95000SourceDFAnovaSSMeanSquareFValuePr>Fkind25221.3000002610.6500003.380.0411TheANOVAProcedureDependentVariable：x2x2SourceDFSumofSquaresMeanSquareFValuePr>FModel2518.533333259.26666?1.620.2078Error5?9148.050000160.492105CorrectedTotal599666.583333R-SquareCoeffVarRootMSEx2Mean0.05364222.9388812.6685555.08333SourceDFAnovaSSMeanSquareFValuePr>Fkind2518.5333333259.266666?1.620.2078TheANOVAProcedureJependentVariable：x3x3SumofSourceDFSquaresMeanSquareFValuePr>FModel22480.83331240.41670.170.8478Error5?427028.50007491.7281CorrectedTotal59429509.3333R-SquareCoeffVarRootMSEx3Mean0.00577621.1798086.5547?408.666?SourceDFAnovaSSMeanSquareFValuePr>Fkind22480.8333331240.41666?0.170.8478x4SourceDFSumofSquaresMeanSquareMode1238529.300019264.6500Error57137115.10002405.5281Correct已HTotzl59175644.4000TheANOVAProcedureDependentVariable：x4FRootMSER-SquareCoeffVarValuePr>F8.010.0009Sourcekind3）多元方差分析CharacteristicRoot0.336045380.123233830.000000000.00000000x4Mean0.21936018.9660449.04618258.6000DFAnova88MeanSquare38529.30000Procedure19264.65000ValuePr>F8.010.0009TheANOVAMultivariateAnalysisofVarianceCharacteristicRootsandVectcrwof:EInverse

H=AnovaSSCPMatrixforkind

E=ErrorSSCPMatrixPercentxlx273.17-0.00045795-0.0037909626.830.004241110.002368780.000.00121062-0.000324010.00-0.003177980.01043526VectorV?EV=1CharacteristicMANOVATestCriteriaandforth已Hypoth已sig=AnovaSSCPMatrixforkindE=ErrorSSCPApproximationgHMatrixStatisticsaIskIe?1y——oowpHRS=2M=0.5ValueN=26ValueNum帶H,where0.000303660.000018420.001570460.00007014ofDFNoOveralDenDF?Lambda0.66635953i?sTrace0.36123585Iing-Law