R语言与概率统计(三) 多元统计分析

> #############6.2一元线性回归分析
> x y plot(x~y)
> lm.sol summary(lm.sol)

Call:
lm(formula = y ~ x)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0431 -0.7056  0.1694  0.6633  2.2653 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   28.493      1.580   18.04 5.88e-09 ***
x            130.835      9.683   13.51 9.50e-08 ***    #所以y=130.835x+28.493,***表示显著性水平，*越多越好
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1    #显著性水平

Residual standard error: 1.319 on 10 degrees of freedom
Multiple R-squared:  0.9481,	Adjusted R-squared:  0.9429   
F-statistic: 182.6 on 1 and 10 DF,  p-value: 9.505e-08    ￥F检验，检验所有系数全是0的假设

> new=data.frame(x=0.16)#怎么预测多个数值的结果？
> lm.pred=predict(lm.sol,new,interval=‘prediction‘,level=0.95)
> lm.pred
       fit      lwr      upr
1 49.42639 46.36621 52.48657

> X 
> forbes plot(forbes$F, forbes$log100)#画出两个变量之间的散点图，观察是否存在线性趋势；学习
> #如何从数据框里面调取向量。怎么写坐标轴的名字和标题？
> #如何从数据框里面调取向量。怎么写坐标轴的名字和标题？
> lm.sol summary(lm.sol)

Call:
lm(formula = log100 ~ F, data = forbes)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.32261 -0.14530 -0.06750  0.02111  1.35924 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -42.13087    3.33895  -12.62 2.17e-09 ***
F             0.89546    0.01645   54.45   abline(lm.sol)#在散点图上添加直线

#残差检验
y.res

#异常值的判断
library(car)
outlierTest(lm.sol)

> outlierTest(lm.sol)
   rstudent unadjusted p-value Bonferroni p
12 12.40369         6.1097e-09   1.0386e-07

> plot(lm.sol)
Hit  to see next plot: return
Hit  to see next plot: return
Hit  to see next plot: return
Hit  to see next plot: return