金融数据挖掘.doc

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 20 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*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 5.38385565 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 0.6047StandardParameter 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 Residual 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 9 300.0000000 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 Squared 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 478.66 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|Intercept -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 Residual1 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 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 Squared 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 136.56027Corrected 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 3.73 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 Type 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 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 21.5268501 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 0.7176x*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 0.1548476 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