Titanic

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3a

 titanic.df <- read.csv(paste("Titanic Data.csv", sep = ""))
View(titanic.df)
dim(titanic.df)

## [1] 889   8

3b

table(titanic.df$Survived)

## 
##   0   1 
## 549 340

3c

prop.table(table(titanic.df$Survived))

## 
##         0         1 
## 0.6175478 0.3824522

3d

 xtabs(Survived~Pclass, data = titanic.df)

## Pclass
##   1   2   3 
## 134  87 119

3e

prop.table(xtabs(Survived~Pclass, data = titanic.df))

## Pclass
##         1         2         3 
## 0.3941176 0.2558824 0.3500000

3f

xtabs(Survived~Pclass+Sex, data = titanic.df)

##       Sex
## Pclass female male
##      1     89   45
##      2     70   17
##      3     72   47

3g

prop.table(xtabs(Survived~Sex, data = titanic.df))

## Sex
##    female      male 
## 0.6794118 0.3205882

3h

mytable <- xtabs(~Survived+ Sex, data = titanic.df)
prop.table(addmargins(mytable),2)

##         Sex
## Survived     female       male        Sum
##      0   0.12980769 0.40554593 0.30877390
##      1   0.37019231 0.09445407 0.19122610
##      Sum 0.50000000 0.50000000 0.50000000

3i

Null hypothesis: proprotion of female survived > proportion of male survived

chisq.test(mytable)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  mytable
## X-squared = 258.43, df = 1, p-value < 2.2e-16

P<0.01 we regect the null hypothesis