多元分析SAS上机实验修改

单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,*,2024/11/27,1,实验,1.,应用,SAS,作一元线性回归分析,data ex;input x y ;,cards;,14.3 46.3 14 30.7 69.3 144.6 22.7 69.2,7.3 16 8 12.3 1.3 2.7 7.9 26.3 20 .,;,proc gplot;,plot y*x; /*,以,y,为纵坐标,以,x,为横坐标*,/,symbol i=rl v=dot; /* i=rl,表示画回归直线*,/,/* v=dot,表示观测值对应的点标记为小圆点*,/,proc reg;model y=x/alpha=0.01 cli xpx i;,output out=a press=presidi; proc print;run;,/*y=x,表示以,y,为因变量,以,x,为自变量,*/,/*,cli,表示要求预测值的,95%,置信区间*,/,/* xpx,表示求增广矩阵, i,表示求逆矩阵*,/,2024/11/27,2,2024/11/27,3,Model: MODEL1,Dependent Variable: Y,Analysis of Variance,Sum of,Mean,Source DF,Squares,Square F Value ProbF,Model 1,14141.74052,14141.74052 140.992 0.0001,Error 6,601.80823,100.30137,C Total 7,14743.54875,Root MSE 10.01506 R-square 0.9592,Dep Mean 43.51250 Adj R-sq 0.9524,C.V. 23.01651,为校正后的决定系数,.,2024/11/27,4,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 0.0001,Model Crossproducts XX XY YY,XX INTERCEP X Y,INTERCEP 8 144.8 348.1,X 144.8 5899.66 13109.99,Y 348.1 13109.99 29890.25,XX Inverse, Parameter Estimates, and SSE,INTERCEP X Y,INTERCEP 0.2249182623 -0.005520346 5.9223664747,X -0.005520346 0.0003049915 2.0768029572,Y 5.9223664747 2.0768029572 601.80822932,2024/11/27,5,Model: MODEL1,Dependent Variable: Y,Analysis of Variance,Sum of,Mean,Source DF,Squares,Square F Value ProbF,Model 1,14141.74052,14141.74052 140.992 0.0001,Error 6,601.80823,100.30137,C Total 7,14743.54875,Root MSE 10.01506 R-square 0.9592,Dep Mean 43.51250 Adj R-sq 0.9524,C.V. 23.01651,为校正后的决定系数,.,2024/11/27,6,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 0.0001,Model Crossproducts XX XY YY,XX INTERCEP X Y,INTERCEP 8 144.8 348.1,X 144.8 5899.66 13109.99,Y 348.1 13109.99 29890.25,XX Inverse, Parameter Estimates, and SSE,INTERCEP X Y,INTERCEP 0.2249182623 -0.005520346 5.9223664747,X -0.005520346 0.0003049915 2.0768029572,Y 5.9223664747 2.0768029572 601.80822932,2024/11/27,7,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 -4.2976,3 144.6 149.8 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 3.556 21.4532 73.4636 .,Sum of Residuals 0,Sum of Squared Residuals 601.8082,Predicted Resid SS (Press) 5610.2160,2024/11/27,8,OBS 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,是第个观测值的预测残差,而,是删去第,i,个观测值后,根据剩下的,n,1,组观测值建立线,性回归方程并将,x,i,代入此回归方程所得到的第,i,个观测,值的预测值,.,2024/11/27,9,多元线性回归,上机实验,2.,例,1.2 P25,data lreg07;input x1 x2 y ;,cards;,1.37 9.08 4.93 11.34 1.89 1.86 9.67,3.06 2.33 0.76 10.2 5.78 17.67 0.05,0.06 15.91 0.73 0.43 15.74 1.03 0.87,5.41 6.25 3.86,5.4 6.3 .,;,symbol i=rl v=star;,/*,（,symbol,为绘图设置语句*,/,/*,i=rl,要求画回归直线*,/,/*v=star,要求用星号表示点）*,/,2024/11/27,10,proc gplot;plot y*x1;,/*,（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）*,/,proc gplot;plot y*x2;,proc reg;model y=x2;run;,proc reg;,model y=x1 x2/alpha=0.01 cli;run;,/*,（要求建立二元线性回归方程,并作检验及回报与预报）*,/,2024/11/27,11,2024/11/27,12,Y=5.621170 - 0.319113*X1;,F=494.064;ProbF,为,0.0001;,R-square=0.9880;,Y=0.363778 + 0.532975*X2;,F=279.602; ProbF,为,0.0001;,R-square=0.9790;,Y=3.610506 - 0.198281*X1 + 0.206755*X2;,F=392.516; ProbF,为,0.0001;,R-square=0.9937;,2024/11/27,13,Model Crossproducts XX XY YY,XX INTERCEP X1 X2 Y,INTERCEP,8 77.87 32.29,20.12,X1,77.87 1066.9317 33.7369,97.2483,X2,32.29 133.7369 40.0809,139.7036,Y 20.12 97.2483 139.7036,82.4468,XX Inverse, Parameter Estimates, and SSE,INTERCEP X1 X2 Y,INTERCEP,22.8232 -1.3771 2.3025,3.6105,X1,-1.3771 0.0841 0.1384,-0.1983,X2,-2.3025 0.1384 0.2368,0.2068,Y 3.6105 -0.1983 0.2068,0.2015,2024/11/27,14,Analysis of Variance,Sum of Mean,Source,DF Squares Square,F Value,Prob F,Model 2 31.64346 15.82173 392.516 0.0001,Error 5 0.20154 0.04031,C Total 7 31.84500,Root MSE,0.20077,R-square,0.9937,Dep Mean 2.51500 Adj R-sq 0.9911,C.V. 7.98289,2024/11/27,15,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 0.139 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 0.0600 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 0.1137 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 预测值 标准误,95%,置信下限与上限 残差（剩余）,2024/11/27,16,例,1.4,逐步回归,data ex; input x1-x4 y ;,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 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;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,定义剔除时的概率水平为,0.2,，,SLE=0.3,定义选入时的概率水平为,0.3,2024/11/27,17,例,1.4,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 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;,2024/11/27,18,Analysis of Variance,Sum of Mean,Source DF 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 20.17663,Parameter Estimates,Parameter Standard Variance,Variable DF Estimate Error t Value Pr |t| Inflation,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 0.3042,137920,x5 1 27.58457 25.69586 1.07 0.3083,308366,2024/11/27,19,Collinearity 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 3.86241,5,0.00000190,1136.53374,Collinearity Diagnostics (intercept adjusted),-Proportion of Variation-,Number x1 x2 x3 x4 x5,1 0.00003290 0.00000152 5.048667E-9 9.142109E-7 5.357538E-7,2 0.12042 2.478475E-8 0.00010018 2.93515E-10 3.689766E-9,3 0.08670 0.00000409 0.00009168 0.00000187 1.28582E-10,4 0.57141 0.00002290 0.00044762 0.00000866 1.232986E-7,5 0.22144,0.99997,0.99936 0.99999 1.00000,2024/11/27,20,从方差膨胀因子分析可知：,x2-x5,有很严重共线性；,从特征值看，第,5,个特征值接近于,0,，且条件指数也非常大，所以，有非常严重的共线性,从方差比值看，,x2-x5,之间有非常大的共线性关系,习题：完成书上的练习,1,2,2024/11/27,21,