金融数据挖掘

1、基于GLM（广义线性模型）的数据分析 SAS里的GLM应用在实际中比较广泛，对数据的分析具有比较强的普适性。趋势面回归分析（Trend Analysis） 是以多元回归分析为理论基础的一种预测与统计技术。它用空间坐标法进行多项式回归，从中估计出最佳的回归模型，因此也被称为趋势面分析，当不知道手中的数据呈线性还是非线性相关时，可以采用趋势面数据分析方法，以便找出拟合数据的最佳统计预测模型。本文运用GLM对一定的数据进行GLM分析。一、 数据与要求此处选取15名吧不同程度的烟民的每日饮酒（啤酒）量与心电图指标（zb）的对应数据。然后设法建立zb与日抽烟量（X）/支和日饮酒量（y）/升之间的关系。序

2、号组别日抽烟量（x）/支日饮酒量（y）/升心电图指标（zb）113010280212511260313513330414014400514514410622012270721811210822512280922513300102231329011340144101234515420133481642514350184501535519470二、 运用GLM过程进行趋势面分析 1. 趋势分析的GLM程序data beer;input obsn x y zb;cards;01 30 10 28002 25 11 26003 35 13 33004 40 14 40005 45 14 41006 2

3、0 12 27007 18 11 21008 25 12 28009 25 13 30010 23 13 29011 40 14 41012 45 15 42013 48 16 42514 50 18 45015 55 19 470;proc glm;model zb=x y/p;proc glm;model zb=x y x*x x*y y*y/p;proc glm;model zb=x y x*x*x x*x*y x*y*y y*y*y/p;proc glm;model zb=x y x*x*x x*x*y x*y*y y*y*y x*x*x*x x*x*x*y x*x*y*y x*y*y

4、*y y*y*y*y/p;run;2. 四种分析模型结果（1）一阶趋势模型Dependent Variable: zb源变量 自由度 平方和 均值 F值 概率值 Sum of Source DF Squares Mean Square F Value Pr F Model 2 90615.20993 45307.60497 127.19 Fx 1 89541.56558 89541.56558 251.36 Fx 1 14652.24351 14652.24351 41.13 |t|Intercept 64.04999380 33.06539919 1.94 0.0766x 5.3838556

5、5 0.83947567 6.41 FModel 5 93330.83580 18666.16716 107.75 FX 1 89541.56558 89541.56558 516.86 Fx 1 965.2913631 965.2913631 5.57 0.0426y 1 127.4395437 127.4395437 0.74 0.4133x*x 1 43.6622972 43.6622972 0.25 0.6277x*y 1 242.0343234 242.0343234 1.40 0.2675y*y 1 49.8430316 49.8430316 0.29 0.6047Standard

6、Parameter Estimate Error t Value Pr |t|Intercept -262.7664793 109.1074817 -2.41 0.0394x 16.0699779 6.8078620 2.36 0.0426y 23.5391327 27.4449867 0.86 0.4133x*x 0.0638773 0.1272383 0.50 0.6277x*y -1.1651016 0.9857119 -1.18 0.2675y*y 1.1673362 2.1762982 0.54 0.6047-Observation Observed Predicted Residu

7、al 1 280.0000000 279.4168700 0.5831300 2 260.0000000 258.6814596 1.3185404 3 330.0000000 351.0997183 -21.0997183 4 400.0000000 388.1251282 11.8748718 5 410.0000000 414.0657505 -4.0657505 6 270.0000000 255.1256024 14.8743976 7 210.0000000 216.6773768 -6.6773768 8 280.0000000 279.9417834 0.0582166 9 3

8、00.0000000 303.5367795 -3.5367795 10 290.0000000 295.5572467 -5.5572467 11 410.0000000 388.1251282 21.8748718 12 420.0000000 419.0280585 0.9719415 13 425.0000000 436.4318573 -11.4318573 14 450.0000000 453.7554706 -3.7554706 15 470.0000000 465.4317699 4.5682301-Sum of Residuals -0.000000Sum of Square

9、d Residuals 1559.164195Sum of Squared Residuals - Error SS -0.000000First Order Autocorrelation -0.354205Durbin-Watson D 2.694808（3）三阶趋势模型Dependent Variable: zb源变量 自由度 平方和 均值 F值 概率值 Sum ofSource DF Squares Mean Square F Value Pr FModel 6 93393.46414 15565.57736 83.21 F x 1 89541.56558 89541.56558 47

10、8.66 Fx 1 1643.347081 1643.347081 8.78 0.0180y 1 197.474017 197.474017 1.06 0.3343x*x*x 1 105.516422 105.516422 0.56 0.4741x*x*y 1 113.710330 113.710330 0.61 0.4580x*y*y 1 146.610010 146.610010 0.78 0.4018y*y*y 1 173.116161 173.116161 0.93 0.3642StandardParameter Estimate Error t Value Pr |t|Interce

11、pt -166.0074589 82.37772231 -2.02 0.0786x 11.1382598 3.75795233 2.96 0.0180y 15.7784340 15.35703905 1.03 0.3343x*x*x -0.0154132 0.02052250 -0.75 0.4741x*x*y 0.1203187 0.15432333 0.78 0.4580x*y*y -0.3416786 0.38595313 -0.89 0.4018y*y*y 0.3134894 0.32587614 0.96 0.3642Observation Observed Predicted Re

12、sidual1 280.0000000 281.0906363 -1.0906363 2 260.0000000 256.0483783 3.9516217 3 330.0000000 351.8935219 -21.8935219 4 400.0000000 390.5707896 9.4292104 5 410.0000000 409.2309652 0.7690348 6 270.0000000 257.9983490 12.0016510 7 210.0000000 220.0483966 -10.0483966 8 280.0000000 275.0160368 4.9839632

13、9 300.0000000 299.4709973 0.5290027 10 290.0000000 295.8228899 -5.8228899 11 410.0000000 390.5707896 19.4292104 12 420.0000000 420.5758580 -0.5758580 13 425.0000000 437.4437284 -12.4437284 14 450.0000000 455.6875798 -5.6875798 15 470.0000000 463.5310833 6.4689167-Sum of Residuals -0.000000Sum of Squ

14、ared Residuals 1496.535862Sum of Squared Residuals - Error SS -0.000000First Order Autocorrelation -0.357545Durbin-Watson D 2.686333- （4） 四阶趋势模型Dependent Variable: zb源变量 自由度 平方和 均值 F值 概率值 Sum ofSource DF Squares Mean Square F Value Pr FModel 11 94480.31919 8589.11993 62.90 0.0029Error 3 409.68081 13

15、6.56027Corrected Total 14 94890.00000R-Square Coeff Var Root MSE zb Mean0.995683 3.367695 11.68590 347.0000Source DF Type I SS Mean Square F Value Pr Fx 1 89541.56558 89541.56558 655.69 0.0001y 1 1073.64435 1073.64435 7.86 0.0676x*x*x 1 2078.77664 2078.77664 15.22 0.0299x*x*y 1 508.85526 508.85526 3

16、.73 0.1491x*y*y 1 17.50614 17.50614 0.13 0.7440y*y*y 1 173.11616 173.11616 1.27 0.3421x*x*x*x 1 52.91566 52.91566 0.39 0.5777x*x*x*y 1 193.81980 193.81980 1.42 0.3192x*x*y*y 1 452.42798 452.42798 3.31 0.1663x*y*y*y 1 40.32879 40.32879 0.30 0.6246y*y*y*y 1 347.36281 347.36281 2.54 0.2090-Source DF Ty

17、pe III SS Mean Square F Value Pr Fx 1 53.8347354 53.8347354 0.39 0.5746y 1 18.4422458 18.4422458 0.14 0.7376x*x*x 1 707.3985134 707.3985134 5.18 0.1073x*x*y 1 688.7276032 688.7276032 5.04 0.1104x*y*y 1 669.2155979 669.2155979 4.90 0.1137y*y*y 1 614.9897506 614.9897506 4.50 0.1239x*x*x*x 1 73.5254957

18、 73.5254957 0.54 0.5162x*x*x*y 1 21.5720987 21.5720987 0.16 0.7176x*x*y*y 1 150.8940383 150.8940383 1.10 0.3704x*y*y*y 1 264.7516451 264.7516451 1.94 0.2581y*y*y*y 1 347.3628138 347.3628138 2.54 0.2090StandardParameter Estimate Error t Value Pr |t|Intercept -748.5352475 602.9093096 -1.24 0.3026x 21.

19、5268501 34.2855706 0.63 0.5746y 63.4532525 172.6669316 0.37 0.7376x*x*x 1.1129083 0.4889782 2.28 0.1073x*x*y -7.8466442 3.4939960 -2.25 0.1104x*y*y 17.6919599 7.9919932 2.21 0.1137y*y*y -12.8173180 6.0398396 -2.12 0.1239x*x*x*x -0.0052895 0.0072088 -0.73 0.5162x*x*x*y -0.0339628 0.0854515 -0.40 0.71

20、76x*x*y*y 0.4218127 0.4012785 1.05 0.3704x*y*y*y -1.0952733 0.7866207 -1.39 0.2581y*y*y*y 0.8411079 0.5273783 1.59 0.2090Observation Observed Predicted Residual1 280.0000000 280.6428697 -0.6428697 2 260.0000000 254.9148649 5.0851351 3 330.0000000 336.2353148 -6.2353148 4 400.0000000 399.8451524 0.15

21、48476 5 410.0000000 409.0029100 0.9970900 6 270.0000000 265.5623644 4.4376356 7 210.0000000 212.0079405 -2.0079405 8 280.0000000 287.4716063 -7.4716063 9 300.0000000 292.6701245 7.3298755 10 290.0000000 295.8090433 -5.8090433 11 410.0000000 399.8451524 10.1548476 12 420.0000000 428.1747562 -8.1747562 13 425.0000000 422.5228