Independence:If event A&B are exclusive by comparing the observed frequencies (those noticed in the sample) , is hampered when there is clear evidence of personal bias Mutually exclusive:A coin toss is an example of a mutually exclusive event. Two events are mutually exclusive if they cannot happen simultaneously. The result of tossing a coin can be either heads or tails, but neither outcome can happen at the same time.

0=T 1=H

s<- data.frame(land = c("down", "up"))
s
##   land
## 1 down
## 2   up

The sample space s is the column lands. The outcomes are down, up and side. so there are 2 posibilities, flip coin 10 times

sample(0:1,10,rep=T)
##  [1] 0 1 1 1 0 0 0 0 1 0

We got 6H, and 4T, write a function to tossing coin n times

TossCoin=function(n) sample (0:1,n,rep=T)
e1=TossCoin(20)
e1
##  [1] 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 0 0 0 0

Now use the sum command to test the probabilities.let’s tossing coin 20 times

sum(e1==0)
## [1] 10
sum(e1==0)/20
## [1] 0.5
sum(e1==1)
## [1] 10
sum(e1==1)/20
## [1] 0.5

We got 10T and 10H, the probability of a trail in this experiment is 0.5 and a Head is also 0.5

mydata <-read.csv("/Users/timyang/Documents/Train CSV/train.csv")
table(mydata$Survived, 
      mydata$Sex, 
      mydata$Pclass)
## , ,  = 1
## 
##    
##     female male
##   0      3   77
##   1     91   45
## 
## , ,  = 2
## 
##    
##     female male
##   0      6   91
##   1     70   17
## 
## , ,  = 3
## 
##    
##     female male
##   0     72  300
##   1     72   47

There are more female survived than male.