myData <- read.csv(paste("Titanic Data.csv"))
head(myData)
##   Survived Pclass    Sex  Age SibSp Parch    Fare Embarked
## 1        0      3   male 22.0     1     0  7.2500        S
## 2        1      1 female 38.0     1     0 71.2833        C
## 3        1      3 female 26.0     0     0  7.9250        S
## 4        1      1 female 35.0     1     0 53.1000        S
## 5        0      3   male 35.0     0     0  8.0500        S
## 6        0      3   male 29.7     0     0  8.4583        Q

Average survivers

aveSur <- aggregate(myData$Age, by=list(myData$Survived), FUN=mean)
aveSur
##   Group.1        x
## 1       0 30.41530
## 2       1 28.42382

The average age of survivers is : 28.42382 The average age of non survivers is: 30.41530

It is observed that average age of survivers is less than tha average age of dead people. This hypothesis will be verified in the next section

T-Test

H0: There is no significant difference between the average age of people of survived and people who dies. H1: The average age of people of died is less than the average age of people who survived

t.test(myData$Age~myData$Survived)
## 
##  Welch Two Sample t-test
## 
## data:  myData$Age by myData$Survived
## t = 2.1816, df = 667.56, p-value = 0.02949
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.1990628 3.7838912
## sample estimates:
## mean in group 0 mean in group 1 
##        30.41530        28.42382

```

value of p = 00.0294 P>0.01 Therefore, we accept the null hypothesis that, There is no significant difference between average age of people who died and average age of people who survived.