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3a
titanic.df <- read.csv(paste("Titanic Data.csv", sep = ""))
View(titanic.df)
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 hypotheis is proportion of female who survived is higher than the proportion of male who 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
since p< 0.01 we reject the null hypothesis that no. of people who are female and survived are independent.