# MATH 2565 W 2007 Section M Week 9 R Script for Confidence Intervals and Tests

```  #
#  MATH 2565 Week 9
#  Inference about means and proportions
#  Chapters 7,8
#

# Chapter 7:  Confidence interval for one mean, one proportion
# Chapter 8:  Hypothesis testing for one mean, one proportion
# Chapter 9:  Confidence intervals and hypothesis testing for two populations

# Estimating one mean

source("http://www.math.yorku.ca/~georges/R/fun.R")
```

```  # Create a population  same as in Chapter 6

dim(baseball)
pop <- baseball[ rep( 1: nrow(baseball), 100), ]
pop <- pop[ !is.na( pop\$sal87 ), ]   # keep only non-missing salaries - to keep it the same as in Chap. 6
```
```  pop\$ba86 <- round(pop\$hits86 / pop\$atbat86 , 4)
dim(pop)
```
```  #
# We need to pretend that we don't have data on the 'population' but we
# can sample individuals and get information from them.
#
# Suppose we want to estimate ba86 and we can get a sample of 10

n <- 10
sam <- sample( pop\$ba86, n)
```
```  sam

# what are we going to do with it to estimate mu = pop. mean of ba86

mean(sam)
```
```  # How accurate an estimate is this??

# Suppose we know (or think we know) that the population SD of Y is 0.03

# SE of mean of (sam)

se.xbar <- 0.03 / sqrt(n)
se.xbar
```
```  # point estimate:
mean(sam)

# point estimate with SE
```
```  mean( pop\$ba86)
se.xbar

# More 'sophisticated' " 95% Confidence Interval"

mean(pop\$ba86)+ c( - 1.96, 1.96) * se.xbar
```

```  # What about 90% CI

# note
pnorm( 1.96) - pnorm(-1.96)
pnorm( 1.96)
alpha <- 1 - .95
alpha

qnorm( 1 - alpha/2)
```
```  # What if we wanted 90%

alpha <- 1 - .9
alpha
crit.value <- qnorm( 1 - alpha/2 )
crit.value    # 1.645

# general formula: CI for mean, Using normal appxn (pop normal or n large),
# popn sigma known

#
#  xbar +/- crit.value x SE(xbar)
#  SE(xbar) = Pop.SD / sqrt(n)
#

ci.1 <- function( x, sigma,  conf = .95 ) {
# CI for one mean, sigma known
xbar <- mean(x)
n <- length(x)
se <- sigma / sqrt(n)
alpha <- 1 - conf
crit.value <- qnorm( 1 - alpha/2)
list( "Confidence Interval for mean, sigma known" = xbar + c(-1,1) * crit.value * se,
"Confidence Level"= conf)
}

ci.1 ( sam, .03 )
ci.1 ( sam, .03, .9)
ci.1 ( sam, .03, .99)

# Thought experiment: what happens if we take 100 samples

cis <- matrix(NA, ncol = 2, nrow = 100)
```
```  for ( ii in 1:100 ) {
sam <- sample(pop\$ba86, 10)
cis[ ii, ] <- ci.1( sam, .03)1
}
cis

plot( x = c(.2,.35), y = c(1,100), type = 'n')

for( ii in 1:100 ) {
lines( x = cis[ii,], y =c( ii, ii), lwd = .5)
}

# Now we get to do something we can only do when we're simulating

abline( v = mean( pop\$ba86), col = 'red')

# What makes a 95% CI a 95% CI?
# The procedure covers the true value 95% of the time.
# With a single sample we have no idea whether we hit or not
# We know that we would miss 5% = 1 - 95% of the time.
# Maybe this is one of them, maybe it's not!!

# Note: we got close to 5% misses BECAUSE our guess of sigma = 0.03 was pretty close
# Note: the number of misses has a binomial distribution or approx Poisson
#

# What would happen if we had a bigger N
```
```  n <- 1000
```
```  cis <- matrix(NA, ncol = 2, nrow = 100)
```
```  for ( ii in 1:100 ) {
sam <- sample(pop\$ba86, 1000)
cis[ ii, ] <- ci.1( sam, .03)1
}
cis
```
```  plot( x = c(.2,.35), y = c(1,100), type = 'n')
```
```  for( ii in 1:100 ) {
lines( x = cis[ii,], y =c( ii, ii), lwd = .5)
}
```
```  # Now we get to do something we can only do when we're simulating
```
```  abline( v = mean( pop\$ba86), col = 'red')

###
###
###   More realistic: sigma not known
###   It would be unusual to know sigma and NOT mu
###

# Idea:
#     1) Use sd(sam) instead of sigma
#     2) Use the 't' distribution instead of z to allow for added error
#        due to uncertainty in sd
```
```  n <- 10
sam <- sample( pop\$ba86 , 10)
sam
xbar <- mean(sam)
xbar
est.sd <- sd(sam)
se.xbar <- est.sd / sqrt(n)
crit.value <- qt( 1 - .05/2, df = n - 1)
crit.value    # note larger

# confidence interval : xbar +/- crit.value x se.xbar

```
```  ci.2 <- function( x,  conf = .95 ) {
# CI for one mean, sigma unknown
xbar <- mean(x)
n <- length(x)
se <- sd(x) / sqrt(n)
alpha <- 1 - conf
crit.value <- qt( 1 - alpha/2, df = n - 1)
list( "Confidence Interval for mean, sigma unknown" = xbar + c(-1,1) * crit.value * se,
"Confidence Level"= conf)
}

ci.2(sam)

###
###  Confidence interval for a proportion
###

```
```  n <- 50
sam <- sample( pop\$league86 == "A", 50)
sam
p <- mean(sam)      # proportion in American league
p

# Use normal appxn

se.p <- sqrt( p * (1-p) / n)
se.p

conf <- .95
alpha <- 1 - conf
alpha
crit.value <- qnorm( 1 - alpha / 2)
crit.value
ci <- p + c(-1,1) * crit.value * se.p
ci
```
```  # true value
mean( pop\$league86 == "A")
```
```  ci.3 <- function( x , conf = .95 ){
# CI for one proportion
# note: x must be 0,1 or F,T
p <- mean(x)
n <- length(x)
se.p <- sqrt( p * (1-p) / sqrt(n))
alpha <- 1 - conf
crit.value <- qnorm( 1 - alpha/2)
if ( n * p < 5 || n * ( 1 - p ) < 5 ) warning("Normal approximation may be invalid")
list( "Confidence Interval for proportion using normal appx" = p + c(-1,1) * crit.value * se.p,
"Confidence Level"= conf)
}
```

1. Chapter 8: Hypothesis testing
2. Answering a question: Do we have evidence to 'prove' that mean(ba) != 0.300
```   ##
## Mean, sigma unknown, using normal appxn (n large or if n small, pop near normal)
##
```
```   # Example: Test whether mean ba == .3 or is it < .3
```
```   mu.0 <- .3
alpha <- .05
```
```   n <- 50
sam <- sample ( pop\$ba86, 50)
sam
```
```   xbar <- mean(sam)
xbar
```
```   se.xbar <- sd(sam) / sqrt(n)
se.xbar
```
```   test.stat <- ( xbar - mu.0 )/ se.xbar
test.stat

# Alternative is left-sided
crit.val <- qt( alpha, df = n - 1)
crit.val

# SO???

# Reject H0 and accept HA that mu < .3
# OR report observed p-value
pt( test.stat, df = n - 1 )

###
###  Hypothesis test for a proportion
###  e.g. proportion in American league
###  HO: popn p = .5 vs !=  (two-tail test)

p.0 <- 0.5
alpha <- .05

n <- 50
sam <- sample ( pop\$league86 == "A" , n)

p <- mean(sam)
p

se.p.0 <- sqrt( p.0 * ( 1 - p.0) / n)
se.p.0

test.stat <- ( p - p.0) / se.p.0
test.stat

#
crit.val <- qnorm( 1 - alpha/2 )
crit.val
```
```   # Reject if test.stat < - crit.val OR test.stat > crit.val
```
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