时间序列分析（3）R语言-最基础的回归模型
来源：互联网 发布：java输入流和输出流 编辑：程序博客网 时间：2024/06/05 14:14
rt<-read.table("exam0203.txt", head=TRUE); rtlm.sol<-lm(Weight~Height, data=rt)summary(lm.sol)rt<-read.table("exam0203.txt", head=TRUE); rt Name Sex Age Height Weight1 Alice F 13 56.5 84.02 Becka F 13 65.3 98.03 Gail F 14 64.3 90.04 Karen F 12 56.3 77.05 Kathy F 12 59.8 84.56 Mary F 15 66.5 112.07 Sandy F 11 51.3 50.58 Sharon F 15 62.5 112.59 Tammy F 14 62.8 102.510 Alfred M 14 69.0 112.511 Duke M 14 63.5 102.512 Guido M 15 67.0 133.013 James M 12 57.3 83.014 Jeffrey M 13 62.5 84.015 John M 12 59.0 99.516 Philip M 16 72.0 150.017 Robert M 12 64.8 128.018 Thomas M 11 57.5 85.019 William M 15 66.5 112.0
summary(lm.sol)Call:lm(formula = Weight ~ Height, data = rt)Residuals: Min 1Q Median 3Q Max -17.6807 -6.0642 0.5115 9.2846 18.3698 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -143.0269 32.2746 -4.432 0.000366 ***Height 3.8990 0.5161 7.555 7.89e-07 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Residual standard error: 11.23 on 17 degrees of freedomMultiple R-squared: 0.7705,Adjusted R-squared: 0.757 F-statistic: 57.08 on 1 and 17 DF, p-value: 7.887e-07
#这是一个最基本的R语言回归模型的结果，其中，按照这些数据，可以得到身高和体重的模型：height=-143.02+3.899*weight，其中，相关系数R—squared,0.77， p-Value:7.887e-7，可以认为是显著的。 0 0
