SPSS典型相关分析结果解读.pdf

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1、CorrelationsforSet-1Y1Y2Y3Y11.0000.9983.5012Y2.99831.0000.5176Y3.5012.51761.0000第一组变量间的简单相关系数CorrelationsforSet-2X1X2X3X4X5X6X7X8X9X10X11X12X13X11.0000-.3079-.7700-.7068-.6762-.7411-.7466-.5922-.1948-.1285-.2650-.9070-.6874X2-.30791.0000-.0117.0103-.0613-.0283-.01

2、40.3333.4161.3810.3831.1098-.0640X3-.7700-.01171.0000.9905.9860.9973.9990.5892.0421-.0196.2492.9515.9903X4-.7068.0103.99051.0000.9910.9935.9952.5634.0249-.0367.2476.9120.9953X5-.6762-.0613.9860.99101.0000.9887.9912.5717.0363-.0277.2475.8972.9926X6-.7411-.0283.9973

3、.9935.98871.0000.9985.5563.0142-.0453.2210.9355.9950X7-.7466-.0140.9990.9952.9912.99851.0000.5795.0319-.0298.2441.9390.9945X8-.5922.3333.5892.5634.5717.5563.57951.0000.7097.6540.8990.6619.5138X9-.1948.4161.0421.0249.0363.0142.0319.70971.0000.9922.8520.1350-.0228X1

4、0-.1285.3810-.0196-.0367-.0277-.0453-.0298.6540.99221.0000.8184.0752-.0801X11-.2650.3831.2492.2476.2475.2210.2441.8990.8520.81841.0000.3093.1840X12-.9070.1098.9515.9120.8972.9355.9390.6619.1350.0752.30931.0000.9040X13-.6874-.0640.9903.9953.9926.9950.9945.5138-.022

5、8-.0801.1840.90401.0000CorrelationsBetweenSet-1andSet-2X1X2X3X4X5X6X7X8X9X10X11X12X13Y1-.7542-.0147.9995.9940.9892.9989.9998.5788.0334-.0280.2426.9430.9937Y2-.7280-.0234.9965.9958.9954.9977.9988.5859.0485-.0136.2573.9285.9949Y3-.4485.2952.5096.4955.5230.4760.5048.

6、9695.7610.7071.9073.5449.4500CanonicalCorrelations11.00021.00031.000第一对典型变量的典型相关系数为CR1=1.....二三Testthatremainingcorrelationsarezero:维度递减检验结果降维检验Wilk'sChi-SQDFSig.1.000.000.000.0002.000.00024.000.0003.000103.48911.000.000此为检验相关系数是否显著的检验，原假设：相关系数为0，每行的检验都是对此行及以后各行所对

7、应的典型相关系数的多元检验。第一行看出，第一对典型变量的典型相关系数不是0的，相关性显著。第二行sig值P=0.000>0.05，在5%显著性水平显著。第三同二。StandardizedCanonicalCoefficientsforSet-1(标准化变量的典型相关的换算系数）123Y112.146-1.52712.981Y2-11.4612.051-13.787Y3-.422.599.986RawCanonicalCoefficientsforSet-1（原始变量的典型相关变量的换算系数）123Y1.002.000.00

8、2Y2.000.000.000Y3-.196.279.458第一个典型变量的标准化典型系数为12.146和-11.461、-0.422。Cv1-1=12.146Y1-11.461Y2-0.422Y3.同上StandardizedCanonicalCoefficientsforSet-2（典型负载系数）（