i123456789101112131415161718192021222324252627X15.683.796.024.854.606.054.907.083.854.654.594.297.976.196.135.716.406.065.096.135.785.436.507.9811.545.843.8427名糖尿病人的资料X2X3X41.904.538.21.647.326.93.566.9510.81.075.888.32.324.057.50.641.4213.68.5012.608.53.006.7511.52.1116.287.90.636.597.11.973.618.71.976.617.81.937.579.91.181.426.92.0610.3510.51.788.538.02.404.5310.33.6712.797.11.032.538.91.715.289.93.362.968.01.134.3111.36.213.4712.37.923.379.810.891.2010.50.928.616.41.206.459.6Y11.28.812.311.613.418.311.112.19.68.49.310.68.49.610.910.114.89.110.810.213.614.916.013.220.013.310.4 y<-c(11.2,8.8,12.3,11.6,13.4,18.3,11.1,12.1,9.6,8.4,9.3,10.6,8.4,9.6,10.9,10.1,14.8,9.1,10.8,10.2,13.6,14.9,16.0,13.2,20.0,13.3,10.4)
x1<-c(5.68,3.79,6.02,4.85,4.60,6.05,4.90,7.08,3.85,4.65,4.59,4.29,7.97,6.19,6.13,5.71,6.40,6.06,5.09,6.13,5.78,5.43,6.50,7.98,11.54,5.84,3.84)
x2<-c(1.90,1.64,3.56,1.07,2.32,0.64,8.50,3.00,2.11,0.63,1.97,1.97,1.93,1.18,2.06,1.78,2.40,3.67,1.03,1.71,3.36,1.13,6.21,7.92,10.89,0.92,1.20)
x3<-c(4.53,7.32,6.95,5.88,4.05,1.42,12.60,6.75,16.28,6.59,3.61,6.61,7.57,1.42,10.35,8.53,4.53,12.79,2.53,5.28,2.96,4.31,3.47,3.37,1.20,8.61,6.45)
x4<-c(8.2,6.9,10.8,8.3,7.5,13.6,8.5,11.5,7.9,7.1,8.7,7.8,9.9,6.9,10.5,8.0,10.3,7.1,8.9,9.9,8.0,11.3,12.3,9.8,10.5,6.4,9.6)
blood<-data.frame(y,x1,x2,x3,x4) #建立数据集 lm.reg<-lm(y~x1+x2+x3+x4,data=blood) #回归
summary(lm.reg) #提取回归结果 lm.step<-step(lm.reg) #逐步回归
summary(lm.step) #提取回归结果 y.res<-residuals(lm.step) #计算残差
print(y.res) #显示y.res的值 y.rst<-rstandard(lm.reg) #计算标准化残差
y.fit<-predict(lm.reg) #取预测值 o