rnorm2 <- function(n,mean,sd) {
mean + sd * scale(rnorm(n))
}
p1 <- rnorm2 (100000000, mean=70, sd=10)
set.seed(101)
s.size <- 16
s1 <- sample(p1, s.size, replace=T)
s1
m.s1 <- mean(s1)
sd.s1 <- sd(s1)
m.s1
sd.s1
se <- sd.s1/sqrt(s.size)
se2 <- 2*se
se3 <- 2*se
m.s1 - se2
m.s1 + se2
# or
c2 <- qnorm(.975)
c3 <- qnorm(.995)
c2
c3
se2 <- c2*se
se3 <- c3*se
m.s1 - se2
m.s1 + se2
> rnorm2 <- function(n,mean,sd) {
> mean + sd * scale(rnorm(n))
> }
>
> p1 <- rnorm2 (100000000, mean=70, sd=10)
>
> set.seed(101)
> s.size <- 16
> s1 <- sample(p1, s.size, replace=T)
> s1
[1] 64.87746 78.68130 50.61449 62.18639 68.08899 63.07744 54.48954 69.26928 55.92358
[10] 70.06123 60.38630 87.91813 78.55023 69.78835 63.15719 64.35994
> m.s1 <- mean(s1)
> sd.s1 <- sd(s1)
> m.s1
[1] 66.33936
> sd.s1
[1] 9.59219
>
> se <- sd.s1/sqrt(s.size)
> se2 <- 2*se
> se3 <- 2*se
>
>
> m.s1 - se2
[1] 61.54327
> m.s1 + se2
[1] 71.13546
>
> # or
> c2 <- qnorm(.975)
> c3 <- qnorm(.995)
> c2
[1] 1.959964
> c3
[1] 2.575829
>
> se2 <- c2*se
> se3 <- c3*se
>
> m.s1 - se2
[1] 61.63928
> m.s1 + se2
[1] 71.03945
>