Spanish <-
  read.table('http://math.uttyler.edu/nathan/classes/statistics/data/spanish-test.data',header=TRUE)

Spanish[1:5,]

boxplot(Spanish)

### This is not correct!  Why?
t.test(Spanish)


#	One Sample t-test
#
#data:  Spanish 
#t = 33.3688, df = 39, p-value < 2.2e-16
#alternative hypothesis: true mean is not equal to 0 
#95 percent confidence interval:
# 26.79592 30.25408 
#sample estimates:
#mean of x 
#   28.525 
#


##### Here is what happened
all.scores <- c(pre.test,post.test)
t.test(all.scores)


pre.test
Spanish$pre.test
attach(Spanish)
pre.test
t.test(post.test-pre.test)

#	One Sample t-test
#
#data:  post.test - pre.test 
#t = 2.2848, df = 19, p-value = 0.03400
#alternative hypothesis: true mean is not equal to 0 
#95 percent confidence interval:
# 0.2056102 4.6943898 
#sample estimates:
#mean of x 
#     2.45 

## With 95% confidence the true increase on test scores is between .21
## and 4.69 points.

## With a p-value of .034 we reject H0 in favor of H1 and conclude that
## there is a difference in pre-test and post-test scores

t.test(post.test-pre.test,alternative = 'greater')


detach()

#### Number 2

Teks <-read.table('http://math.uttyler.edu/nathan/classes/statistics/data/teks-confidence.data',header=TRUE)

hist(Teks$conf)

attach(Teks)
t.test(conf[ecert==1],conf[ecert==0],alternative='less')

#	Welch Two Sample t-test
#
#data:  conf[ecert == 1] and conf[ecert == 0] 
#t = -2.6083, df = 39.922, p-value = 0.00637
#alternative hypothesis: true difference in means is less than 0 
#95 percent confidence interval:
#      -Inf -0.493619 
#sample estimates:
#mean of x mean of y 
# 2.500000  3.892857 
#