Titanic Data

W1D5 Answers to Q2 and Q3 are as follows :

2(b)

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

3(a)

#View(titanic.df)
nrow(titanic.df)

## [1] 889

3(b)

table(titanic.df$Survived) 

## 
##   0   1 
## 549 340

another way of 3(b)

sum(titanic.df$Survived)

## [1] 340

3(c)

prop.table(table(titanic.df$Survived))*100

## 
##        0        1 
## 61.75478 38.24522

3(d)

mytable11 <- xtabs(~ Survived + Pclass , data = titanic.df)
mytable11

##         Pclass
## Survived   1   2   3
##        0  80  97 372
##        1 134  87 119

3(e)

prop.table(mytable11 , 2)*100

##         Pclass
## Survived        1        2        3
##        0 37.38318 52.71739 75.76375
##        1 62.61682 47.28261 24.23625

3(f)

mytable22 = xtabs(~ Pclass + Sex + Survived , data = titanic.df)
ftable(mytable22)

##               Survived   0   1
## Pclass Sex                    
## 1      female            3  89
##        male             77  45
## 2      female            6  70
##        male             91  17
## 3      female           72  72
##        male            300  47

3(g)

mytable33 <- xtabs(~ Sex + Survived , data = titanic.df)
prop.table(mytable33,2)*100

##         Survived
## Sex             0        1
##   female 14.75410 67.94118
##   male   85.24590 32.05882

3(h)

prop.table(mytable33,1)*100

##         Survived
## Sex             0        1
##   female 25.96154 74.03846
##   male   81.10919 18.89081

3(i)

chisq.test(mytable33)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  mytable33
## X-squared = 258.43, df = 1, p-value < 2.2e-16

Since the two variable Survived and Sex are dependent and we have that propotion of females survived greater than that of males survived, so we confirm the hypothesis.