beta0<-0.2
beta1<-4
sigma<-sqrt(1.62)
x1<-rnorm(n=25,mean=4,sd=1.1)
epsilon<-rnorm(n=25,mean=0,sd=sigma)
y<-beta0+beta1*x1+epsilon
x1
y
e<-summary(lm(y~x1))$residuals
p<-((1:25)-0.5)/25
ee<-sort(e)
plot(ee,p,main="Normal Probability Plot",xlab="e")
abline(lm(p~ee))
summary(lm(y~x1))$coefficients
summary(lm(y~x1))$r.squared
summary(lm(y~x1))$sigma
anova(lm(y~x1))
library(MASS)
summary(rlm(y~x1,psi=psi.huber, method="M",init="lts"))$coefficients
summary(rlm(y~x1,psi=psi.huber, method="M",init="lts"))$sigma
summary(rlm(y~x1,psi=psi.hampel, method="M",init="lts"))$coefficients
summary(rlm(y~x1,psi=psi.hampel, method="M",init="lts"))$sigma
summary(rlm(y~x1,psi=psi.bisquare, method="M",init="lts"))$coefficients
summary(rlm(y~x1,psi=psi.bisquare, method="M",init="lts"))$sigma
summary(rlm(y~x1, method="MM"))$coefficients
summary(rlm(y~x1, method="MM"))$sigma
lmsreg(y~x1)$coefficients
x1<-c(x1,-30,45,50)
y<-c(y,50,100,120)
plot(x1,y)
e<-summary(lm(y~x1))$residuals
p<-((1:28)-0.5)/28
ee<-sort(e)
plot(ee,p,main="Normal Probability Plot",xlab="e")
abline(lm(p~ee))
summary(lm(y~x1))$coefficients
summary(lm(y~x1))$r.squared
summary(lm(y~x1))$sigma
anova(lm(y~x1))
summary(rlm(y~x1,psi=psi.huber, method="M",init="lts"))$coefficients
summary(rlm(y~x1,psi=psi.huber, method="M",init="lts"))$sigma
summary(rlm(y~x1,psi=psi.hampel, method="M",init="lts"))$coefficients
summary(rlm(y~x1,psi=psi.hampel, method="M",init="lts"))$sigma
summary(rlm(y~x1,psi=psi.bisquare, method="M",init="lts"))$coefficients
summary(rlm(y~x1,psi=psi.bisquare, method="M",init="lts"))$sigma
summary(rlm(y~x1, method="MM"))$coefficients
summary(rlm(y~x1, method="MM"))$sigma
lmsreg(y~x1)$coefficients