final

## jack
data <- read.csv('Libro1.csv',header = T)
data=as.matrix(data$gpa)
n=length(data)

t=median(data)

reps <- matrix(0,n)
mues.t=rep(0,n-1)

for (i in 1:n) {
  mues.t[]=data[-i]
  tmp=median(mues.t)
  reps[i]=tmp

}
reps
#mediana estimada
mureps=mean(reps)

##error estandar
se.jack=sqrt(((n-1)/n)*sum((reps-mureps)^2))
se.jack

#sesgo
bias.jack=(n-1)*(mureps-t)
bias.jack
####################
## boot
dat=read.csv('forearm.csv')
dat=as.matrix(dat)
n=length(dat)
B=100

### Calculo de la asimetria
skewness=function(x) {

  skew=median(x)
  skew}

theta=skewness(dat)
theta

### Error estandar de la simetria
m=vector()
thetam=vector()

for (i in 1:B) {
  m=sample(dat,replace=T)
  thetam[i]=skewness(m)
}
r=sd(thetam)
r
### Estimacion del sesgo
meanb=mean(thetam)
biasb=meanb-theta
biasb