Distribución normal
x=rnorm(10000,5.0,1.0) #Genera número aleatorios media=5.0 sd=1.0
hist(x,freq=FALSE,breaks=50,col="green") #graficando el histograma de x
xx=seq(0,9,0.1)
lines(xx,dnorm(xx,5.0,1.0),lwd=2)
alfa=0.05 # 1-alfa=0.95
dc1=qnorm(alfa/2,5.0,1.0)
dc2=qnorm(1-alfa/2,5.0,1.0)
cat("dc1=",dc1,"dc2=",dc2,"\n")
## dc1= 3.040036 dc2= 6.959964
abline(v=dc1)
abline(v=dc2)
p1=pnorm(dc1,5.0,1.0)
p2=pnorm(dc2,5.0,1.0)
cat("p1=",p1,"p2=",p2)
## p1= 0.025 p2= 0.975
Distribución t-student
alfa=0.05 #nivel de significancia
gl=14 #grados de libertad
x=rt(10000,gl)
hist(x,freq=FALSE,breaks=50,col="#5768DD",main="Distribución t-student")
xx=seq(-6,6,0.1)
lines(xx,dt(xx,gl),lwd=2)
dc1=qt(alfa/2,gl)
dc2=qt(1-alfa/2,gl)
cat("dc1=",dc1,"dc2=",dc2,"\n")
## dc1= -2.144787 dc2= 2.144787
abline(v=dc1)
abline(v=dc2)
p1=pt(dc1,gl)
p2=pt(dc2,gl)
cat("p1=",p1,"p2=",p2)
## p1= 0.025 p2= 0.975
n=15
x=rnorm(n,4.0,0.8)
xx=seq(0,8,0.1)
media=mean(x)
std=sd(x)
t=(media-4.0)/(std/sqrt(n))
cat("t=",t)
## t= 0.1667002
t.test(x,mu=4.0)
##
## One Sample t-test
##
## data: x
## t = 0.1667, df = 14, p-value = 0.87
## alternative hypothesis: true mean is not equal to 4
## 95 percent confidence interval:
## 3.562065 4.511748
## sample estimates:
## mean of x
## 4.036906