多元统计分析实验报告计算协方差矩阵相关矩阵SAS.docx

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1、（一）院 系：数学与统计学学院专 业：统计学年 级：2009级课程名称：统计分析学 号：姓 名：指导教师：2012年 4月28日（一）实验名称1.编程计算样本协方差矩阵和相关系数矩阵;根据多元分析结果，P指小于，表明在的显着水平下，四个变量有显 着差异。2.多元方差分析MANOVA。（二）实验目的1 .学习编制sas程序计算样本协方差矩阵和相关系数矩阵;2 .对数据进行多元方差分析。（三）实验数据第一题：xlx2x3x4x5x6x74462178182406218518544451561684240166172385517818047581761764070176180436416217044

2、63174176384817018644451681684556186192455117617647471621645450166170494418018551571681725110481621684848162164497616816857581741765462156165524816416650481461555148172172544416817251591861885749148155495618618848521701765253170172第二题:kindxlx2x3x4112560338210111980233330163512602031655142915011306540

3、320516945350190146605852001146662732501875458524011107750727011076036420011306139120018045429270160504421901815426028011358750726015748400285175525202601766540325015542411170266544553102824540321026565312280240514772802675448129323850468210242453511902113403903102805552020027660507189294332602802605

4、142919025540390295265484811772694844222521256331227021205641628027045468370262664162242696037728036533480260310034468295365634162653117484682503114633953803553054623536451507320311090442225360624402483110693772603887829936037363390320311455494240310354416310310033273312314061312345380362862503135544

5、6834531306932536036057273260（四）实验内容1 .打开SAS软件并导入数据；2 .编制程序计算样本协方差矩阵和相关系数矩阵;3 .编制sas程序对数据进行多元方差分析；4 .根据实验结果解决问题，并撰写实验报告；（五）实验体会（结论、评价与建议等）第一题：程序如下：proc corr data= cov;proc corr data= nosimple cov;with x3 x4;partial xl x2;run;结果如下:(1)协方差矩阵7变量： xlx2x3x4x5x6x7协方差矩阵，自由度=30xlx2x3x4x5xGx7xlxl27.1591398-10.

6、1364946-8.45626451.364709?-6.1193548-18.0516129-20.6752688x2x2-10.136494669.3650389-7.22105081.65825471.568204315.498655919.0337204x3x38.4562645-7.221050828.3793848-6.3725471-15.3064183-21.7352376-11.5574785x4 x41.36470971.6582547-6.37254711.92491784.60930114.46124732.8747634x5x5-6.11935481.5682043-

7、15.30641834.609301168.797849527.038709719.5731183x6x6-18.051612915.4986559-21.73523764.461247327.0387097105.103225887.3505376x7x7-20.675268819.0337204-11.55747852.874763419.573118387.350537683.9806452SAS系统CORR过程08:15 Friday, April 28, 20001(2)相关系数矩阵Pearson相关系数,N = 31当 HO： Rho=0 时，Prob |r|xlx2x3x4x5x

8、6x7xl1.00000-0.23354-0.304590.18875-0.14157-0.33787-0.43292xl0.20610.09570.30920.44750.06300.0150x2-0.233541.00000-0.162750.143510.022700.181520.24938x20.20610.38170.44120.90350.32840.1781x3-0.30459-0.162751.00000-0.86219-0.34641-0.31797-0.23874x30.09570.3817.00010.05630.02660.1997x40.188750.14351-0

9、.862191.000000.400540.313650.22610x40.30920.4412.00010.02560.08580.2213x5-0.141570.02270-0.346410.400541.000000.317970.25750x50.44750.90350.05630.02560.08130.1620xG-0.337870.18152-0.397970.313650.317971.000000.92975x60.06300.32840.02660.08580.0813.0001x7-0.432920.24938-0.236740.226100.257500.929751.

10、00000x70.01500.17610.19970.22130.1620 x2、x3、x4的方差分析The ANOVA ProcedureDependent Variable： xl xlSourceModelErrorCorrected TotalSum ofDFSquares25221.300005744069.550005949290.85000Mean Square F Value2610.65000773.150003.38Pr F0.0411xl Mean85.95000R-Square Coeff Var Root MSE0.10592832.3508727.80557R-Sq

11、uare Coeff Var0.05364222.99888Root MSE x2 Mean12.6685555.08333SourceDF Anova SS Mean Square F Valuekind2518.5333333259.26666671.62Pr F0.2078SourcekindDF2Anova SS5221.300000Mean Square2610.650000F Value3.38Dependent Variable： x2 x2The ANOVA ProcedureSum ofSourceDFSquaresMean SquareF ValueModel2518.53

12、3333259.2666671.62ErrorCorrected Total57599148.0500009666.583333160.492105Pr F0.0411Pr F0.2078dependent Variable： x3 x3The ANOVA ProcedureSourceModelErrorCorrected TotalDF25759Sum of Squares2480.8333427028.5000429509.3333Mean Square1240.41677491.7281F Value0.17Pr F0.8478R-SquareCoeff VarRootMSEx3Mea

13、n0.00577621.1798086.55477408.6667SourceDFAnova SSMean SquareF ValuePr Fkind22480.8333331240.4166670.170.8478The ANOVA ProcedureDependent Variable： x4 x4Sum ofSourceDFSquaresMean SquareF ValuePr FModel238529.300019264.65008.010.0009Error57137115.10002405.5281Corrected Total59175644.4000R-Square Coeff

14、 Var Root MSEx4 Mean0.21936018.9660449.04618258.6000SourcekindDF2Anova SS38529.30000Mean Square19264.65000F Value Pr F8.010.0009（3）多元方差分析The ANOVA ProcedureMultivariate Analysis of VarianceCharacteristic Roots and Vectors of： E Inverse 米 H, whereH = Anova SSCP Matrix for kindE = Error SSCP MatrixCha

15、racterist ic RootPercentCharacteristic Vector V，EV=1x3x4xlx20.3360453873.17-0.00045795-0.003790960.000303660.002793390.1232338326.830.004241110.002368780.000018420.000028320.000000000.000.00121062-0.000324010.00157046-0.000065390.000000000.00-0.003177980.010435260.000070140.00076972MANOVA Test Crite

16、ria and F Approximations for the Hypothesis of No OveralI kind EffectH = Anova SSCP Matrix for kindE = Error SSCP MatrixS=2M=0.5N=26Stat ist icValueF ValueNum DF Den DF Pr FWilks Lambda0.66635953Pillais Trace0.36123585Hotel Iing-Law leyTrace0.45927921Roys Greatest Root0.336045384372000633341080.00401100.004174.8560.0048550.0027NOTE： F Statistic for Roy s Greatest Root is an upperbound.NOTE： F Statistic for Wilks Lambda is exact.