### The dataset is "America's Best Small Companies"
###
### http://www.stat.sc.edu/webstat/version2.0/data/ceo.dat
###
### bootstrap

### First create a dir C:\Stat6601\ and then save the data file ceo.txt there from the website.

### load the data

CEO <- matrix(scan("C:\\Stat6601\\ceo.txt"),ncol=2,byrow=T)	# scans ceo.txt, the data file,
																		# into a matrix

AGE <- CEO[,1]		# assigns the first column to AGE vector
SALARY <- CEO[,2]		# assigns the second column to SALARY vector

### plot the AGE data

hist(AGE,probability = T)		# histogram

plot(density(AGE),type="l")  	# density plot

### summarize the AGE data

mean.AGE <- mean(AGE)
mean.AGE

sd.AGE <- sqrt(var(AGE))
sd.AGE

n <- length(AGE)
n

se.AGE <- sd.AGE/sqrt(n)
se.AGE

### confidence interval for the population mean of AGE

t.out <- t.test(AGE)

t.out$conf.int

### bootstrap the mean

boot.AGE <- bootstrap(AGE,mean)		# help(jackknife)

summary(boot.AGE)

limits.emp(boot.AGE)

# Question: Why are we using the mean with the AGE data?

# Question: How does the traditional t-confidence interval compare to the bootstrap interval?

### Repeat the analysis for the SALARY data.

# Question: What is an appropriate statistics to estimate?

hist(SALARY,probability = T)		# histogram

plot(density(SALARY),type="l")  	# density plot