Let S200 be the number of heads that turn up in 200 tosses of a fair coin.
Estimate
Using the Binomial Probability function
\[ P(x=k)= \left( \begin{bmatrix} n \\ k \end{bmatrix}\right)p^k(1-p)^{n-k}\]
The binomial distribution model is used for finding the probability of success of an event which has only two possible outcomes in a series of experiments.
R has four in-built functions to generate binomial distribution.
# number of trial = 200
# probability of head = 0.5
# number of head = 100
n <- 200
p <- 0.5
k <- 100
dbinom<-dbinom(k,n,p)
dbinom
## [1] 0.05634848
# number of trial = 200
# probability of head = 0.5
# number of head = 100
n <- 200
p <- 0.5
k <- 90
dbinom<-dbinom(k,n,p)
dbinom
## [1] 0.02079869
# number of trial = 200
# probability of head = 0.5
# number of head = 100
n <- 200
p <- 0.5
k <- 80
dbinom<-dbinom(k,n,p)
dbinom
## [1] 0.001025104
A true-false examination has 48 questions. June has probability 3/4 of answering a question correctly. April just guesses on each question. A passing score is 30 or more correct answers. Compare the probability that June passes the exam with the probability that April passes it.
pbinom(x, size, prob) - the cumulative probability of an event. It is a single value representing the probability.
x is a vector of numbers.
p is a vector of probabilities.
n is number of observations.
size is the number of trials.
prob is the probability of success of each trial.
Mean = n * p Variance = n * p * (1 - p)
Calculating the probability that June passes the exam
1 - pnorm(30.5, mean=48*3/4, sd=sqrt(48*3/4*1/4))
## [1] 0.9666235
Calculating the probability that April passes the exam
1 - pnorm(30.5, mean=48*1/2, sd=sqrt(48*1/2*1/2))
## [1] 0.03030098