modify this code from the exponential distribution to poisson distribution and calculate the mean and standard error respectively: ##### exponential_bootstrap.r ### get nerve data nerve = read.csv("nerve.csv",header=T)[,1] n = length(nerve) xbar = mean(nerve) lambda.mle = 1/xbar # MLE for lambda ### parametric bootstrap B = 100000 # number of bootstrap samples btstrp = rep(0,B) # for bootstrap estimates for (i in 1:B) btstrp[i] = 1/mean(rexp(n,lambda.mle)) cat("bootstrap mean =",mean(btstrp),"st. error =",sd(btstrp)," ") hist(btstrp) ### get 95% confidence intervals and compare quantile(btstrp,c(0.025,0.975)) c(1/mean(nerve)-1.96*sd(btstrp),1/mean(nerve)+1.96*sd(btstrp)) ### nonparametric bootstrap for (i in 1:B) btstrp[i] = 1/mean(sample(nerve,replace=TRUE)) cat("bootstrap mean =",mean(btstrp),"st. error =",sd(btstrp)," ") hist(btstrp) ### get 95% confidence intervals and compare quantile(btstrp,c(0.025,0.975)) c(1/mean(nerve)-1.96*sd(btstrp),1/mean(nerve)+1.96*sd(btstrp))