Runners <- read.table('http://math.uttyler.edu/nathan/classes/statistics/data/jan23-runners.data')
Runners[1:5,]

names(Runners)

# either Runners$weight or attach(runners) and then use weight

attach(Runners)
hist(weight)
boxplot(weight)

t.test(weight)

#        One Sample t-test
#
#data:  weight 
#t = 62.9639, df = 23, p-value < 2.2e-16
#alternative hypothesis: true mean is not equal to 0 
#95 percent confidence interval:
# 59.76152 63.82181 
#sample estimates:
#mean of x 
# 61.79167 

# the 95% confidence interval for average male runner weight is 59.76 to 63.82

# from this we're sure that the mean weight should be less than 65kg

# we're not 95% confident that the average male runner weight is less than 63.5
#
#  but ...
#
#
t.test(weight,alternative='less')

#        One Sample t-test
#
#data:  weight 
#t = 62.9639, df = 23, p-value = 1
#alternative hypothesis: true mean is less than 0 
#95 percent confidence interval:
#     -Inf 63.47363 
#sample estimates:
#mean of x 
# 61.79167 

# so if we really only want an upper bound, we can say that the 
# average male runner weight is less than 63.5kg

detach(Runners)

Vitamin.C <- read.table('http://math.uttyler.edu/nathan/classes/statistics/data/jan23-vitaminc.data',header=TRUE)
attach(Vitamin.C)
names(Vitamin.C)
boxplot(factory,haiti)

t.test(factory)
t.test(haiti)
t.test(factory-haiti)
#        One Sample t-test
#
#data:  factory - haiti 
#t = 4.9589, df = 26, p-value = 3.745e-05
#alternative hypothesis: true mean is not equal to 0 
#95 percent confidence interval:
# 3.122616 7.544050 
#sample estimates:
#mean of x 
# 5.333333 

# average vitamin C loss is somewhere between 3.12 and 7.54 units

detach(Vitamin.C)


Malathion <- read.table('http://math.uttyler.edu/nathan/classes/statistics/data/jan23-malathion.data',header=TRUE)
attach(Malathion)
names(Malathion)

boxplot(control,treatment)

t.test(control,treatment)
#        Welch Two Sample t-test
#
#data:  control and treatment 
#t = 5.9869, df = 17.625, p-value = 1.265e-05
#alternative hypothesis: true difference in means is not equal to 0 
#95 percent confidence interval:
# 1.438438 2.997459 
#sample estimates:
#mean of x mean of y 
# 3.384615  1.166667 
#
# we're 95% confident that the difference between means of the two groups
# is between 1.43 and 2.99 (of course this is control - treatment so 
# we're doing  average number less on treated stems)