mu_100
sigma_10
Count_0
M_1000
alpha_.05
pvector_c()

for(i in 1:M) {
	x_rnorm(30,mu,sigma)
	t.output_t.test(x, mu=mu)
	pvalue_t.output$p.value
	pvector_c(pvector, pvalue)
	if (pvalue < alpha) Count_Count + 1	
}

alpha.est_Count/M
print(alpha.est)
hist(pvector)
qqnorm(pvector)
bootstrap(pvector,mean,B=1000)