用SPSS进行主成分回归实例分析.pdf

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1、如何利用SPSS进行主成分回归实例分析主成分回归分析数据编辑、定义格式x1x2x3x4x5Y15.572463472.92184.45566.5244.0220481339.759.56.92696.8220.423940620.2512.84.281033.1518.746505568.3336.73.91603.6249.257231497.635.75.51611.3744.92115201365.83244.61613.2755.485779168743.35.621854.1759.2859691639.9246.75.152160.5594.39846128

2、72.3378.76.182305.58128.02201063655.08180.56.153503.939613313291260.95.883571.89131.42107713921103.74.883741.4127.21155433865.67126.85.54026.52252.9361947684.1157.7710343.81409.23470312446.33169.410.7811732.17463.73920414098.4331.47.0515414.94510.228653315524371.66.3518854.45第一步，进行一般的线性回

3、归分析：首先给出各个变量的平均值，标准差，膨胀系数VIF，以便进行多重共线性诊断。变量平均值标准差膨胀系数VIFx1148.27588161.038589597.57076x218163.2352921278.110557.94059x34480.618244906.642068933.08650x4106.31765107.9541523.29386x55.893531.584074.27984y4978.480005560.53359然后给出各个变量之间的相关系数(右上角为显著水平)。x1x2x3x4x5yx11.000000.000000.000000.00000

4、0.003180.00000x20.907381.000000.000000.000000.072280.00000x30.999900.907151.000000.000000.003180.00000x40.935690.910470.933171.000000.061350.00000x50.671200.446650.671110.462861.000000.01497y0.985650.945170.985990.940360.578581.00000以及一般线性回归模型分析结果：方差分析表方差来源平方和df均方F值显著水平回归490177488.121655

5、98035497.62433237.790080.00000剩余4535052.3673511412277.48794总的494712540.489001630919533.78056相关系数R=0.995406，决定系数RR=0.990833，调整相关R'=0.993311变量x回归系数标准系数偏相关标准误t值显著水平b01962.948031071.361661.832200.09184b1-15.85167-0.45908-0.0488897.65299-0.162330.87375b20.055930.214030.621480.021262.630990.02

6、194b31.589621.402690.153183.092080.514090.61652b4-4.21867-0.08190-0.174527.17656-0.587840.56754b5-394.31413-0.11233-0.49331209.63954-1.880910.08446剩余标准差sse=642.08838，Durbin-Watsond=2.73322。第二步，对自变量进行主成分分析，给出主成分分析结果：No特征值百分率%累计百分率%14.1971283.9423483.9423420.6674813.3496897.2920230.094631.

7、8926699.1846940.040710.8142399.9989250.000050.00108100.00000并显示如下选择主成分个数的用户操作界面：特征向量(转置)x1x2x3x4x5z10.485290.453240.484980.460970.33374z2-0.00203-0.33561-0.00085-0.310800.88925z3-0.166230.80424-0.15396-0.537200.11524z4-0.468190.18747-0.509260.633860.29073z5-0.71948-0.0011