发布时间 : 星期三 文章利用主成分分析法对我国各地区普通高等教育的发展水平进行综合评价 - 图文更新完毕开始阅读3db1b8a5ba0d4a7302763ab4
5辽宁 0.7621 6吉林 0.5884 7黑龙江 0.2971 8湖北 0.2455 9江苏 0.0581 10广东 0.0058 11四川 -0.268 12山东 -0.3645 13甘肃 -0.4879 14湖南 -0.5065 15浙江 -0.7016 16新疆 -0.7428 17福建 -0.7697 18山西 -0.7965 19河北 -0.8895 20安徽 -0.8917 21云南 -0.9557 22江西 -0.9610 23海南 -1.0147 24内蒙古 -1.1246 25西藏 -1.1470 26河南 -1.2059 27广西 -1.2250 28宁夏 -1.2513 29贵州 -1.6514 30青海 -1.68 附录1计算标准化矩阵Z 程序
>> load gj.txt %把原始数据保存在纯文本文件gj.txt中 >> gj=zscore(gj) %数据标准化 gj =
4.3685 3.9057 4.0909 4.1392 4.5401 4.5748 2.4120 0.3954 4.1567 2.4056
1.9862 2.6869 2.3854 2.4187 2.0965 1.9157 0.8299 1.1346 0.8758 2.0174
1.0221 1.4520 1.5048 1.3575 0.9509 1.0406 1.4024 1.0991 0.7748 0.6992
0.0952 0.2331 0.1895 0.2072 0.1326 0.1823 0.0558 0.5375 1.6834 0.0947
0.2342 0.3453 0.3790 0.3951 0.0988 0.1823 0.7080 0.7219 -0.0328 0.0353
0.3918 0.3133 0.2898 0.2270 0.1495 0.1823 0.5775
-0.2813 0.5224 -0.0664
-0.0717 -0.0556 -0.0111 -0.0169 -0.0536 -0.0533 0.8638 0.2482 0.0681 0.3716
-0.1829 0.0086 -0.0223 -0.0136 -0.0649 -0.0701 0.4691 0.7675 0.2700 -0.1540
-0.2756 -0.0396 0 -0.0466 -0.1383 -0.1374 0.2405 1.0602 -0.4114 0.0566
-0.5166 -0.4405 -0.2564 -0.3168 -0.3696 -0.3899 0.7418 1.0264 -0.4619 1.4909
-0.6371 -0.4245 -0.4124 -0.4091 -0.3696 -0.4067 0.4234 1.2987 -0.0076 0.0192
-0.6279 -0.1358 -0.3344 -0.3959 -0.3922 -0.4235 0.4793 1.3884 -0.6890 -0.3377
-0.4981 -0.3924 -0.3567 -0.3663 -0.3414 -0.3562 -0.3371 0.4664 0.4467 -0.1460
-0.4703 -0.3924 -0.3678 -0.3531 -0.3696 -0.3899 0.4979 0.4005 -0.2095 -0.4695
-0.3590 -0.3924 -0.2564 -0.3201 -0.3414 -0.3562 -0.0305 -0.0309 -0.7647 0.0236
0.0396 -0.3122 -0.2341 -0.1191 -0.0705 -0.0196 -0.7098 -0.5435 -0.4619 -0.7741
-0.1922 -0.2160 -0.2564 -0.2740 -0.3584 -0.3562 -0.1881 -0.4775 -0.6638 -0.2167
-0.3683 -0.2160 -0.3233 -0.2740 -0.2850 -0.2889 -0.7606 0.2939 -0.3104 -0.8300
-0.4054 -0.3764 -0.3121 -0.3729 -0.3696 -0.4067 -0.0509 -0.1155 -0.6133 -0.7802
-0.6093 -0.5047 -0.5239 -0.5113 -0.4543 -0.4572 0.4590 0.1806 -0.5628 -0.8107
-0.5444 -0.4886 -0.6019 -0.5640 -0.4656 -0.4740 -0.2660 -0.6889 -0.1842 0.5871
-0.4425 -0.3764 -0.3455 -0.3531 -0.3358 -0.4067 -0.2220 0.2262 -0.5376 -1.4308
-0.5074 -0.5367 -0.4793 -0.4487 -0.4486 -0.4909 -0.4709 -0.0630 -0.6890 0.1136
-0.3776 -0.3764 -0.5128 -0.4289 -0.3471 -0.3057 -0.4184 -0.5908 -0.5880 -0.8296
0.4103 -0.6490 -0.5462 -0.5410 -0.2906 -0.2384 -3.0524 -2.6580 1.1281 2.6339
-0.6464 -0.5528 -0.5350 -0.5640 -0.4656 -0.5077 -0.2897 -0.0681 -0.6385 -0.7762
-0.6001 -0.6169 -0.5685 -0.5673 -0.4938 -0.5077 0.3065 -0.3980 -0.7900 -1.0072
0.1322 -0.2962 -0.3567 -0.3070 -0.2793 -0.2216 -1.2569
-1.4908 -0.3357 -0.9116
-0.5630 -0.6971 -0.6911 -0.6860 -0.5051 -0.5245 -0.3388 -1.5432 -0.5376 -0.8963
0.2157 -0.4565 -0.5350 -0.4948 -0.3584 -0.2889 -2.0750 -2.2960 -0.4366 -0.1114 附录2构造矩阵相关系数矩阵R
程序
>> r=corrcoef(gj) r =
1.0000 0.9434 0.9528 0.9591 0.9746 0.9798 0.4065 0.0663 0.8680 0.6609
0.9434 1.0000 0.9946 0.9946 0.9743 0.9702 0.6136 0.3500 0.8039 0.5998
0.9528 0.9946 1.0000 0.9987 0.9831 0.9807 0.6261 0.3445 0.8231 0.6171
0.9591 0.9946 0.9987 1.0000 0.9878 0.9856 0.6096 0.3256 0.8276 0.6124
0.9746 0.9743 0.9831 0.9878 1.0000 0.9986 0.5599 0.2411 0.8590 0.6174
0.9798 0.9702 0.9807 0.9856 0.9986 1.0000 0.5500 0.2222 0.8691 0.6164
0.4065 0.6136 0.6261 0.6096 0.5599 0.5500 1.0000 0.7789 0.3655 0.1510
0.0663 0.3500 0.3445 0.3256 0.2411 0.2222 0.7789 1.0000 0.1122 0.0482
0.8680 0.8039 0.8231 0.8276 0.8590 0.8691 0.3655 0.1122 1.0000 0.6833
0.6609 0.5998 0.6171 0.6124 0.6174 0.6164 0.1510 0.0482 0.6833 1.0000
附录3计算特征值，贡献率 程序
>> [x,y,z]=pcacov(r) x =
0.3497 -0.1972 -0.1639 -0.1022 -0.0188 0.7499 -0.4301 -0.0307 -0.2182
0.3590 0.0343 -0.1084 -0.2266 -0.1893 -0.4302 -0.5078 -0.0507 0.0906
0.3623 0.0291 -0.0900 -0.1692 -0.0828 0.1742 0.6032 -0.5913 0.1153
0.3623 0.0138 -0.1128 -0.1607 -0.1083 0.0301 0.4216 0.7735 -0.1459
-0.0725 0.5633 0.2592 0.1416 0.3605 -0.0507 -0.1534 -0.0442 -0.0112 -0.5275 -0.4397 0.0135 -0.2029 -0.5735
0.3602 -0.0646 -0.1645 -0.0032 0.0115 -0.4921 -0.0820 -0.0665 0.0825 0.7608
0.2241 0.5826 -0.0397 0.0812 0.7678 0.0800 0.0343 -0.0632 0.0164 -0.0283
0.1201 0.7021 0.3577 0.0702 -0.5514 -0.1757 0.1344 -0.0802 -0.0116 -0.0161
0.3192 -0.1941 0.1204 0.8999 -0.0512 0.1774 -0.0296 0.0216 0.0037 -0.0240
0.2452 -0.2865 0.8637 -0.2457 -0.0332 -0.0104 0.0119 0.0147 y =
7.5022 1.5770 0.5362 0.2064 0.1450 0.0222 0.0071 0.0027 0.0007 0.0006 z =
75.0216 15.7699 5.3621 2.0638 1.4500 0.2219 0.0712 0.0266 0.0073
0.0057
附录4将特征向量标准化 程序
>> f=repmat(sign(sum(x)),size(x,1),1) >> x=x.*f
0.2208 -0.0332