CS计算机代考程序代写 # Drinkers vs. Tee-totallers

# Drinkers vs. Tee-totallers

x <- rep(c(0:7),c(22,6,18,23,18,10,3,0)) length(x) sum(x) 7*100 - sum(x) # => p|x ~ Beta(252,450)

# posterior predictive distribution

# how many zeroes in a sample of 100?

set.seed(123)
p.post <- rbeta(10^5, 252, 450) post.pred.zero <- numeric(10^5) for(i in 1:10^5){ post.pred.zero[i] <- sum(rbinom(100,7,p.post[i]) == 0) } hist(post.pred.zero,seq(-.5,20.5,1),col="plum") mean(post.pred.zero>=22)

### take another look at the data

hist(x, seq(-.5,7.5,1), col=’orange’)