Assignment

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Q1a. its quantitative variables for both Passengerid and age. the level of measurement of passengerid is nominal and for the age is ratio.

Q1b. age has the most missing observations

mydata %>% select(everything())%>% summarise_all(funs(sum(is.na(.))))

Q2.

mydata\(Age[is.na(mydata\)Age)]<-median(mydata\(Age, na.rm=TRUE) mydata\)SibSp[is.na(mydata\(SibSp)]<-median(mydata\)SibSp, na.rm=TRUE) mydata\(Parch[is.na(mydata\)Parch)]<-median(mydata$Parch, na.rm=TRUE)

Q3

describe(mydata\(Age) describe(mydata\)SibSp) describe(mydata$Parch)

Q4

table(mydata\(Survived, mydata\)Sex) I found Felmale has much higher survival rate than male.

Q5

boxplot(mydata\(Age~mydata\)Survived, notch=TRUE, horizontal=T)

I notice the medians of people survived and not survived are the same. which means the average age of people survived or no are the same. but the maximum and minimum value of people survived are wider.