# File "Rboot".

B = 1000

# Some data (same as in file "Rjack").
x = c(0.417123602237552, 0.357405868126079, 0.711339075583965,
0.946056608809158, 0.173700894461945, 0.105324893025681,
0.310572318034247, 0.120300267590210, 0.80332009983249,
0.347110137576237)

n = length(x)

thetahatlist = NULL

# Run the bootstrap, with B resamplings of n resamples each.

for (b in 1:B) {

    # Do n bootstrap resamples.
    resampled = rep(0,n)
    for (i in 1:n) {
	j = floor( runif(1,1,n+1) )  # uniform on {1,2,...,n}
	resampled[i] = x[j]
    }

    # Compute thetahat for this resample.
    thetahat = mean(resampled)

    thetahatlist = c(thetahatlist, thetahat)

}

# Compute the bootstrap estimate of mean, thetastar.
thetastar = sum(thetahatlist) / B

# Compute the bootstrap estimate of variance.
sqsum = 0
for (i in 1:B) {
    sqsum = sqsum + (thetahatlist[i] - thetastar)^2
}
varest = sqsum / (B-1)

print(varest)