多元分析SAS上机实验修改ppt课件

1、Y0102030405060708090100110120130140150X010203040506070Model: MODEL1Dependent Variable: YAnalysis of Variance Sum of MeanSource DF Squares Square F Value ProbFModel 1 14141.74052 14141.74052 140.992 0.0001Error 6 601.80823 100.30C Total 7 14743.54875 Root MSE 10.01506 R-square 0.9592 Dep Mean 43.5125

2、0 Adj R-sq 0.9524C.V. 23.016511)1(222 pnRpRRjdA为校正后的决议系数为校正后的决议系数. Parameter Estimates Parameter Standard T for H0:Variable DF Estimate Error Parameter=0 Prob |T|INTERCEP 1 5.922366 4.74969580 1.247 0.2589 X 1 2.076803 0.17490302 11.874 0.0001Model Crossproducts XX XY YYXX INTERCEP X YINTERCEP 8 144

3、.8 348.1X 144.8 5899.66 13109.99Y 348.1 13109.99 29890.25XX Inverse, Parameter Estimates, and SSE INTERCEP X YINTERCEP 0.2249182623 -0.005520346 5.9223664747 X -0.005520346 0.0003049915 2.0768029572 Y 5.9223664747 2.0768029572 601.80822932Model: MODEL1Dependent Variable: YAnalysis of Variance Sum of

4、 MeanSource DF Squares Square F Value ProbFModel 1 14141.74052 14141.74052 140.992 0.0001Error 6 601.80823 100.30C Total 7 14743.54875 Root MSE 10.01506 R-square 0.9592 Dep Mean 43.51250 Adj R-sq 0.9524C.V. 23.016511)1(222 pnRpRRjdA为校正后的决议系数为校正后的决议系数. Parameter Estimates Parameter Standard T for H0:

5、Variable DF Estimate Error Parameter=0 Prob |T|INTERCEP 1 5.922366 4.74969580 1.247 0.2589 X 1 2.076803 0.17490302 11.874 0.0001Model Crossproducts XX XY YYXX INTERCEP X YINTERCEP 8 144.8 348.1X 144.8 5899.66 13109.99Y 348.1 13109.99 29890.25XX Inverse, Parameter Estimates, and SSE INTERCEP X YINTER

6、CEP 0.2249182623 -0.005520346 5.9223664747 X -0.005520346 0.0003049915 2.0768029572 Y 5.9223664747 2.0768029572 601.80822932 Dep Var Predict Std Err Lower95% Upper95%Obs Y Value Predict Predict Predict Residual 1 46.3000 35.6206 3.603 9.5773 61.6640 10.6794 2 30.7000 34.9976 3.613 8.9459 61.0493 -4.

7、2976 3 144.6 149.8 9.630 115.8 183.8 -5.2448 4 69.2000 53.0658 3.631 26.9988 79.1327 16.1342 5 16.0000 21.0830 4.013 -5.3172 47.4833 -5.0830 6 12.3000 22.5368 3.957 -3.8127 48.8863 -10.2368 7 2.7000 8.6222 4.601 -18.3464 35.5908 -5.9222 8 26.3000 22.3291 3.965 -4.0274 48.6856 3.9709 9 . 47.4584 3.55

8、6 21.4532 73.4636 . Sum of Residuals 0 Sum of Squared Residuals 601.8082 Predicted Resid SS (Press) 5610.2160OBS X Y PRESIDI 1 14.3 46.3 12.2667 2 14.0 30.7 -4.9405 3 69.3 144.6 -69.4832 4 22.7 69.2 18.5761 5 7.3 16.0 -6.0554 6 8.0 12.3 -12.1305 7 1.3 2.7 -7.5067 8 7.9 26.3 4.7089)(iyyPRESIDiii 是第个观

9、测值的预测残差是第个观测值的预测残差,而而 )(iyi是删去第是删去第i个观测值后个观测值后,根据剩下的根据剩下的n1组观测值建立线组观测值建立线性回归方程并将性回归方程并将xi代入此回归方程所得到的第代入此回归方程所得到的第i个观测个观测值的预测值值的预测值. 多元线性回归多元线性回归上机实验上机实验2. 例例1.2 P25data lreg07;input x1 x2 y ;cards;1.37 9.08 4.93 11.34 1.89 1.86 9.673.06 2.33 0.76 10.2 5.78 17.67 0.050.06 15.91 0.73 0.43 15.74 1.03 0

10、.875.41 6.25 3.86 5.4 6.3 .;symbol i=rl v=star;/*symbol 为绘图设置语句为绘图设置语句*/* i=rl 要求画回归直线要求画回归直线*/*v=star 要求用星号表示点要求用星号表示点*/proc gplot;plot y*x1;/*要求画回归直线及点要求画回归直线及点x1,y*/proc reg;model y=x1/xpx i;run;/*要求建立一元线性回归方程并作检验要求建立一元线性回归方程并作检验*/ proc gplot;plot y*x2;proc reg;model y=x2;run;proc reg; model y=x1

11、 x2/alpha=0.01 cli;run;/*要求建立二元线性回归方程要求建立二元线性回归方程 并作检验及报答与预告并作检验及报答与预告*/Y0123456X1024681012141618Y0123456X2012345678910 11Y=5.621170 - 0.319113*X1;F=494.064;ProbF为为0.0001;R-square=0.9880;Y=0.363778 + 0.532975*X2;F=279.602; ProbF为为0.0001;R-square=0.9790;Y=3.610506 - 0.198281*X1 + 0.206755*X2;F=392.51

12、6; ProbF为为0.0001;R-square=0.9937;Model Crossproducts XX XY YYXX INTERCEP X1 X2 YINTERCEP 8 77.87 32.29 20.12X1 77.87 1066.9317 33.7369 97.2483X2 32.29 133.7369 40.0809 .7036Y 20.12 97.2483 .7036 82.4468 XX Inverse, Parameter Estimates, and SSE INTERCEP X1 X2 YINTERCEP 22.8232 -1.3771 2.3025 3.6105X1

13、 -1.3771 0.0841 0.4 -0.1983X2 -2.3025 0.4 0.2368 0.2068Y 3.6105 -0.1983 0.2068 0.2021 Analysis of Variance Sum of MeanSource DF Squares Square F Value Prob FModel 2 31.64346 15.82173 392.516 0.0001Error 5 0.20214 0.04031C Total 7 31.84500Root MSE 0.20077 R-square 0.9937Dep Mean 2.51500 Adj R-sq 0.99

14、11C.V. 7.98289 1)1(*:222 pnRpRRqsRjdA Dep Var Predict Std Err Lower95% Upper95%Obs Y Value Predict Predict Predict Residual 1 4.9300 5.2162 0.120 4.6150 5.8174 -0.2862 2 1.8600 1.7528 0. 1.1252 2.3803 0.1072 3 2.3300 2.3258 0.122 1.7221 2.9295 0.00420 4 5.7800 5.5687 0.154 4.9188 6.2186 0.2113 5 0.0

15、600 0.1172 0.132 -0.4998 0.7343 -0.0572 6 0.4300 0.6068 0.104 0.0251 1.1884 -0.1768 7 0.8700 0.7025 0.110 0.1 1.2914 0.1675 8 3.8600 3.8300 0.092 3.2626 4.3974 0.0300 9 . 3.8423 0.090 3.2763 4.4084 . 观测值观测值 预测值预测值 规范误规范误 95%置信下限与上限置信下限与上限 残差剩余残差剩余例例1.4 逐渐回归逐渐回归data ex; input x1-x4 y ;cards;9.2 2.73

16、1.47 1.14 1.16 9.1 3.73 1.82 0.83 1.15 8.6 4.88 1.83 2.13 1.84 10.23 3.97 1.59 1.35 1.36 5.6 3.73 1.84 1.82 0.86 5.37 4.24 1.87 1.35 0.9 6.13 3.15 1.99 1.65 0.12 8.2 4.65 1.62 4.57 0.9 8.8 4.38 1.54 2.07 1.93 7.6 3.86 1.6 2.42 1.1 9.7 4.38 1.69 1.52 1.4 8.37 5.1 1.81 2.28 1.76 12.17 4.9 1.73 1.58 1.

17、64 10.27 3.73 1.61 1.2 1.47 8.9 4.47 1.88 0.8 0.92 8.23 5.28 1.7 3.07 1.5;proc reg;model y=x1-x4/selection=rsquare b;model y=x1-x4/selection=rsquare adjrsq cp AIC MSE SSE collinoint;run;proc reg;model y=x1-x4/selection=stepwise sle=0.3 sls=0.2; run; / * sls is the level spcified by user*/SLS=0.2定义剔除

18、时的概率程度为定义剔除时的概率程度为0.2，SLE=0.3定义选定义选入时的概率程度为入时的概率程度为0.3例例1.4 共线性诊断共线性诊断data ex; input x1-x4 y ; x5=int(x2+x3+x4)*90)/90;cards;9.2 2.73 1.47 1.14 1.16 9.1 3.73 1.82 0.83 1.15 8.6 4.88 1.83 2.13 1.84 10.23 3.97 1.59 1.35 1.36 5.6 3.73 1.84 1.82 0.86 5.37 4.24 1.87 1.35 0.9 6.13 3.15 1.99 1.65 0.12 8.2

19、4.65 1.62 4.57 0.9 8.8 4.38 1.54 2.07 1.93 7.6 3.86 1.6 2.42 1.1 9.7 4.38 1.69 1.52 1.4 8.37 5.1 1.81 2.28 1.76 12.17 4.9 1.73 1.58 1.64 10.27 3.73 1.61 1.2 1.47 8.9 4.47 1.88 0.8 0.92 8.23 5.28 1.7 3.07 1.5;proc reg data=ex;model y=x1-x5/vif collinoint;run;Analysis of Variance Sum of Mean Source DF

20、 Squares Square F Value Pr F Model 5 2.55497 0.51099 8.03 0.0028 Error 10 0.63672 0.06367 Corrected Total 15 3.19169 Root MSE 0.25233 R-Square 0.8005 Dependent Mean 1.25063 Adj R-Sq 0.7008 Coeff Var 20.17663Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inf

21、lation Intercept 1 2.82510 1.33994 2.11 0.0612 0 x1 1 0.02693 0.06221 0.43 0.6743 2.85303 x2 1 -27.00023 25.70336 -1.05 0.3182 76421 x3 1 -29.73236 25.68616 -1.16 0.2740 3263.98863 x4 1 -27.81537 25.68282 -1.08 0.3042 920 x5 1 27.58457 25.69586 1.07 0.3083 308366Collinearity Diagnostics (intercept adjusted) Condition Number Eigenvalue Index 1 2.45588 1.00000 2 1.48451 1.28621 3 0.89498 1.65652 4 0.16462 3.86241 5 0.00000190 1.53374 Collinearity Diagnostics (interce