《金融数据挖掘与应用》课程作业
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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 组别 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 日抽烟量（x）/支 30 25 35 40 45 20 18 25 25 23 40 45 48 50 55 日饮酒量（y）/升 10 11 13 14 14 12 11 12 13 13 14 15 16 18 19 心电图指标（zb） 280 260 330 400 410 270 210 280 300 290 410 420 425 450 470 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 10 280 02 25 11 260
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《金融数据挖掘与应用》课程作业
03 35 13 330 04 40 14 400 05 45 14 410 06 20 12 270 07 18 11 210 08 25 12 280 09 25 13 300 10 23 13 290 11 40 14 410 12 45 15 420 13 48 16 425 14 50 18 450 15 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 <.0001 Error 12 4274.79007 356.23251 Corrected Total
14 94890.00000
R-Square Coeff Var Root MSE zb Mean 0.954950 5.439228 18.87412 347.000
--------------------------------------------------------------------------------------------------------------------------------- Source DF Type I SS Mean Square F Value Pr > F x 1 89541.56558 89541.56558 251.36 <.0001 y 1 1073.64435 1073.64435 3.01 0.1081
--------------------------------------------------------------------------------------------------------------------------------- Source DF Type III SS Mean Square F Value Pr > F
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《金融数据挖掘与应用》课程作业
x 1 14652.24351 14652.24351 41.13 <.0001 y 1 1073.64435 1073.64435 3.01 0.1081
---------------------------------------------------------------------------------------------------------------------------------
Standard
Parameter Estimate Error t Value Pr > |t| Intercept 64.04999380 33.06539919 1.94 0.0766 x 5.38385565 0.83947567 6.41 <.0001 y 6.94199869 3.99872078 1.74 0.1081
Observation Observed Predicted Residual
1 280.0000000 294.9856503 -14.9856503
2 260.0000000 275.0083707 -15.0083707 3 330.0000000 342.7309246 -12.7309246 4 400.0000000 376.5922015 23.4077985 5 410.0000000 403.5114798 6.4885202 6 270.0000000 255.0310911 14.9689089 7 210.0000000 237.3213811 -27.3213811 8 280.0000000 281.9503694 -1.9503694 9 300.0000000 288.8923681 11.1076319 10 290.0000000 278.1246568 11.8753432 11 410.0000000 376.5922015 33.4077985 12 420.0000000 410.4534785 9.5465215 13 425.0000000 433.5470441 -8.5470441 14 450.0000000 458.1987528 -8.1987528 15 470.0000000 492.0600298 -22.0600298
--------------------------------------------------------------------------------------------------------------------------------- Sum of Residuals -0.000000 Sum of Squared Residuals 4274.790069 Sum of Squared Residuals - Error SS -0.000000 First Order Autocorrelation 0.235461 Durbin-Watson D 1.362704
（2）二阶趋势模型
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 5 93330.83580 18666.16716 107.75 <.0001 Error 9 1559.16420 173.24047 Corrected Total
14 94890.00000
R-Square Coeff Var Root MSE zb Mean 0.983569 3.793108 13.16208 347.0000
--------------------------------------------------------------------------------------------------------------------------------
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《金融数据挖掘与应用》课程作业
Source DF Type I SS Mean Square F Value Pr > F X 1 89541.56558 89541.56558 516.86 <.0001 y 1 1073.64435 1073.64435 6.20 0.0345 x*x 1 1892.86626 1892.86626 10.93 0.0091 x*y 1 772.91658 772.91658 4.46 0.0638 y*y 1 49.84303 49.84303 0.29 0.6047 Source DF Type III SS Mean Square F Value Pr > F x 1 965.2913631 965.2913631 5.57 0.0426 y 1 127.4395437 127.4395437 0.74 0.4133 x*x 1 43.6622972 43.6622972 0.25 0.6277 x*y 1 242.0343234 242.0343234 1.40 0.2675 y*y 1 49.8430316 49.8430316 0.29 0.6047
Standard
Parameter Estimate Error t Value Pr > |t| Intercept -262.7664793 109.1074817 -2.41 0.0394 x 16.0699779 6.8078620 2.36 0.0426 y 23.5391327 27.4449867 0.86 0.4133 x*x 0.0638773 0.1272383 0.50 0.6277 x*y -1.1651016 0.9857119 -1.18 0.2675 y*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 -0.000000 Sum of Squared Residuals 1559.164195 Sum of Squared Residuals - Error SS -0.000000 First Order Autocorrelation -0.354205 Durbin-Watson D 2.694808
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《金融数据挖掘与应用》课程作业
（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 of
Source DF Squares Mean Square F Value Pr > F Model 6 93393.46414 15565.57736 83.21 <.0001 Error 8 1496.53586 187.06698 Corrected Total
14 94890.00000
R-Square Coeff Var Root MSE zb Mean 0.984229 3.941569 13.67724 347.0000
Source DF Type I SS Mean Square F Value Pr > F x 1 89541.56558 89541.56558 478.66 <.0001 y 1 1073.64435 1073.64435 5.74 0.0435 x*x*x 1 2078.77664 2078.77664 11.11 0.0103 x*x*y 1 508.85526 508.85526 2.72 0.1377 x*y*y 1 17.50614 17.50614 0.09 0.7675 y*y*y 1 173.11616 173.11616 0.93 0.3642
--------------------------------------------------------------------------------------------------------------------------------- Source DF Type III SS Mean Square F Value Pr > F x 1 1643.347081 1643.347081 8.78 0.0180 y 1 197.474017 197.474017 1.06 0.3343 x*x*x 1 105.516422 105.516422 0.56 0.4741 x*x*y 1 113.710330 113.710330 0.61 0.4580 x*y*y 1 146.610010 146.610010 0.78 0.4018 y*y*y 1 173.116161 173.116161 0.93 0.3642 Standard
Parameter Estimate Error t Value Pr > |t|
Intercept -166.0074589 82.37772231 -2.02 0.0786 x 11.1382598 3.75795233 2.96 0.0180 y 15.7784340 15.35703905 1.03 0.3343 x*x*x -0.0154132 0.02052250 -0.75 0.4741 x*x*y 0.1203187 0.15432333 0.78 0.4580 x*y*y -0.3416786 0.38595313 -0.89 0.4018 y*y*y 0.3134894 0.32587614 0.96 0.3642 Observation Observed Predicted Residual
1 280.0000000 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
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(金融保险)金融数据挖掘与应用课程作业
