人均一般预算性财政收入 第三产业占GDP比重(%) 人均社会消费品零售额 人均实际利用外资额（万美元/人） 人均城乡居民储蓄存款 农民人均纯收入 在岗职工平均工资 人才密度指数 科技支出占财政支出比重（%） 每万人拥有执业医师数量 每千人拥有病床数 -1.261 -1.929 -1.786 1.034 -2.680 4.533 .949 -1.360 5.461 .508 .217 -.435 -.302 -2.036 .651 1.572 -2.484 -.428 1.450 -2.555 -7.602 6.858 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Scores.
该表格是因子得分矩阵。这是根据回归算法计算出来的因子得分函数的系数，根据这个表格可以看出下面的因子得分函数。
F1=-0.054x1+0.003x2+0.100x3-0.090x4+0.046x5-0.083x6-0.068x7+0.000x8+3.170x9+ 0.495x10-2.090x11-0.549x12+1.365x13
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SPSS根据这13个因子的得分函数，自动计算2-个样本的3个引子得分，并且将3个引子得分作为新变量，保存在SPSS数据编辑窗口中（分别为FAC1_1、FAC2_1、FAC3_1、FAC4_1、FAC5_1、FAC6_1、FAC7_1、FAC8_1、FAC9_1、FAC10_1、FAC11_1、FAC12_1、FAC13_1）
（11）SPSS输出的该部分的结果如下： Component Score Covariance Matrix Component 1 2 3 4 5 6 7 8 9 10 11 12 13 1 1.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 2 .000 1.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 3 .000 .000 1.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 4 .000 .000 .000 1.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 5 .000 .000 .000 .000 1.000 .000 .000 .000 .000 .000 .000 .000 .000 6 .000 .000 .000 .000 .000 1.000 .000 .000 .000 .000 .000 .000 .000 7 .000 .000 .000 .000 .000 .000 1.000 .000 .000 .000 .000 .000 .000 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Scores. Component Score Covariance Matrix Component 1 2 3 4 5 6 7 8 9 10 11 12 13 8 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 .000 .000 .000 9 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 .000 .000 10 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 .000 11 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 12 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 13 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 Component Score Covariance Matrix Component 1 2 3 4 5 6 7 8 9 10 11 12 13 8 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 .000 .000 .000 9 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 .000 .000 10 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 .000 11 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 12 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 13 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Scores. 该输出部分是因子变量的协方差矩阵。在前面已经说明，所得到的因子变量应该是正交、不相关的。从协方差矩阵看，不同因子之间的数据为0，因而也证实了银子之间是不相关的。
课程作业
选择自己感兴趣的数据（自己建立亦可），进行主成分分析，并对结果进行简要解释，可将结果与上次课中聚类分析结果进行对比。
