Ako sa naučiť analýzu po tretie

data(iris)
setosa <- iris[iris$Species == "setosa",]
attach(setosa)
plot(Petal.Length, Petal.Width)


petal_lm <- lm(Petal.Width ~ Petal.Length)
abline(petal_lm)
summary(petal_lm)


hist(residuals(petal_lm))
deviance(petal_lm)

predict(petal_lm, newdata = data.frame(Petal.Length=6),
interval="prediction")

predictions <- predict(petal_lm, newdata = setosa, interval="prediction")
plot(Petal.Length, Petal.Width)
lines(predictions[,1] ~ setosa, col=1)
lines(predictions[,2] ~ setosa, col=1, lty=2)
lines(predictions[,3] ~ setosa, col=1, lty=2)


detach(setosa)
versicol=iris[iris$Species=='versicolor',]
attach(versicol)
versi_lm=lm(Petal.Width ~ Petal.Length)
predict(petal_lm, newdata = data.frame(Petal.Length=6),
interval="prediction")
summary(versi_lm)


rozsireny=lm(Petal.Width ~ Petal.Length+Sepal.Width)
summary(rozsireny)

cele=iris
detach(versicol)
attach(cele)
model=lm(Petal.Width ~ Petal.Length+Species)
summary(model)

x1=runif(50,1,10)
x2=runif(50,1,10)
x3=runif(50,1,10)
y=0.5+2*x1+1.5*x2+x3+x4
sum=rnorm(length(y),0,0.5)
predx1=lm(y~x1)
predx2=lm(y~x2)
predx3=lm(y~x3)
summary(predx3)
residuals(predx3)

x4=2*x1
predx4=lm(y~x1+x4+x2+x3)
summary(predx4)
cor(x1,x4)

predx5=lm(y~x4+x2+x3)
summary(predx5)