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lab4 solutions - for(i in 1:500 means[i< mean(runif(2...

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# lab4 # 1. x <- rnorm(2) mean(x) mean(rnorm(2)) mean(rnorm(2)) mean(rnorm(2)) ## the means tend to be close to 0, but they can sometimes differ more than a little from 0. # 2. means <- c() for (i in 1:500) { means[i] <- mean(rnorm(2)) } hist(means) # 3. means <- c() for (i in 1:500) { means[i] <- mean(rnorm(10)) } hist(means) # 4. means <- c() for (i in 1:500) { means[i] <- mean(rnorm(17)) } hist(means) means <- c() for (i in 1:500) { means[i] <- mean(rnorm(25)) } hist(means) means <- c() for (i in 1:500) { means[i] <- mean(rnorm(50)) } hist(means) means <- c() for (i in 1:500) { means[i] <- mean(rnorm(100)) } hist(means) # 4. (ahem, again). means <- c()
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Unformatted text preview: for (i in 1:500) { means[i] <- mean(runif(2)) } plot(0,0,type="n", xlim=c(0,1), ylim=c(0,10), xlab="density estimate", ylab="f(x)") lines(density(means)) means <- c() for (i in 1:500) { means[i] <- mean(runif(10)) } lines(density(means)) means <- c() for (i in 1:500) { means[i] <- mean(runif(17)) } lines(density(means)) means <- c() for (i in 1:500) { means[i] <- mean(runif(25)) } lines(density(means)) means <- c() for (i in 1:500) { means[i] <- mean(runif(50)) } lines(density(means)) means <- c() for (i in 1:500) { means[i] <- mean(runif(100)) } lines(density(means))...
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