R语言线性回归数据分析案例可视化报告附代码数据

bmidiabetesagecla033.60.627501bmidiabetesagecla033.60.627501026.60.351310023.30.6723219428.10.16721016843.12.288331025.60.201300bmidiabetesagecla#161487235#21856629#38183640#41896623#501374035#65116740pairs(pima,gap=0.5,033.60.627501026.60.351310023.30.6723219428.10.16721016843.12.288331025.60.201300col='navyblue')R语言线性回归数据分析案例可视化报告从源下载数据集。pima<-read.table("/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data",header=F,sep=",")2.清理数据2.a放入数据列colnames(pima)<-c("npreg","glucose","bp","triceps","insulin","bmi","diabetes","age","class")head(pima)#npregglucosebptricepsinsulinssTOC\o"1-5"\h\z#161487235#21856629#38183640#41896623#501374035#65116740head(pima)#npregglucosebptricepsinsulinss

pimalm<-Im(class-npreg+glucose+bp+triceps+insulin+bmi+diabetes+age,data=pima)summary(pimalm)###Call:#lm(formula=class~npreg+glucose+bp+triceps+insulin+#bmi+diabetes+age,data=pima)##Residuals:#Min1QMedian3QMax#-1.01348-0.29513-0.095410.321121.24160##Coefficients:#EstimateStd.ErrortvaluePr(>|t|)#(Intercept)-0.85389430.0854850-9.989<2e-16***#npreg0.02059190.00513004.0146.56e-05***#bp-0.00233190.0008116-2.8730.00418**#triceps0.00015450.00111220.1390.88954#insulin-0.00018050.0001498-1.2050.22857#bmi0.01324400.00208786.3443.85e-10***#diabetes0.14723740.04505393.2680.00113**#age0.00262140.00154861.6930.09092.#---#Signif.codes:0'***'0.001***'0.01'*'0.05'.'0.1''1##Residualstandarderror:0.4002on759degreesoffreedom#MultipleR-squared:0.3033,AdjustedR-squared:0.2959#F-statistic:41.29on8and759DF,p-value:<2.2e-16去除大p值的变量(p值>0.005)pimalm<-update(pimalm,.~.-triceps,data=pima)summary(pimalm)##Call:#lm(formula=class~npreg+glucose+bp+insulin+bmi+diabetes+#age,data=pima)##Residuals:#Min1QMedian3QMax#-1.01707-0.29614-0.096560.320731.24183##Coefficients:#EstimateStd.ErrortvaluePr(>|t|)#(Intercept)-0.85379060.085