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This tutorial explains how to work with the binomial distribution in R using the functions **dbinom**, **pbinom**, **qbinom**, and **rbinom**.

**dbinom**

The function **dbinom **returns the value of the probability density function (pdf) of the binomial distribution given a certain random variable *x*, number of trials (size) and probability of success on each trial (prob). The syntax for using dbinom is as follows:

**dbinom(x, size, prob) **

Put simply, **dbinom **finds the probability of getting a certain number of* *successes **(x)** in a certain number of trials **(size)** where the probability of success on each trial is fixed **(prob)**.

The following examples illustrates how to solve some probability questions using dbinom.

**Example 1:** *Bob makes 60% of his free-throw attempts. If he shoots 12 free throws, what is the probability that he makes exactly 10?*

#find the probability of 10 successes during 12 trials where the probability of #success on each trial is 0.6 dbinom(x=10, size=12, prob=.6) # [1] 0.06385228

The probability that he makes exactly 10 shots is** 0.0639**.

**Example 2:** *Sasha flips a fair coin 20 times. What is the probability that the coin lands on heads exactly 7 times?*

#find the probability of 7 successes during 20 trials where the probability of #success on each trial is 0.5 dbinom(x=7, size=20, prob=.5) # [1] 0.07392883

The probability that the coin lands on heads exactly 7 times is **0.0739**.

**pbinom**

The function **pbinom **returns the value of the cumulative density function (cdf) of the binomial distribution given a certain random variable *q*, number of trials (size) and probability of success on each trial (prob). The syntax for using pbinom is as follows:

**pbinom(q, size, prob) **

Put simply, **pbinom **returns the area to the left of a given value *q** *in the binomial distribution. If you’re interested in the area to the right of a given value *q*, you can simply add the argument **lower.tail = FALSE**

**pbinom(q, size, prob, lower.tail = FALSE) **

The following examples illustrates how to solve some probability questions using pbinom.

**Example 1:*** Ando flips a fair coin 5 times. What is the probability that the coin lands on heads more than 2 times?*

#find the probability of more than 2 successes during 5 trials where the #probability of success on each trial is 0.5 pbinom(2, size=5, prob=.5, lower.tail=FALSE) # [1] 0.5

The probability that the coin lands on heads more than 2 times is** 0.5**.

**Example 2:*** Suppose Tyler scores a strike on 30% of his attempts when he bowls. If he bowls 10 times, what is the probability that he scores 4 or fewer strikes?*

#find the probability of 4 or fewer successes during 10 trials where the #probability of success on each trial is 0.3 pbinom(4, size=10, prob=.3) # [1] 0.8497317

The probability that he scores 4 or fewer strikes is **0.8497**.

**qbinom**

The function **qbinom **returns the value of the inverse cumulative density function (cdf) of the binomial distribution given a certain random variable *q*, number of trials (size) and probability of success on each trial (prob). The syntax for using qbinom is as follows:

**qbinom(q, size, prob) **

Put simply, you can use **qbinom **to find out the p^{th} quantile of the binomial distribution.

The following code illustrates a few examples of **qbinom **in action:

#find the 10th quantile of a binomial distribution with 10 trials and prob #of success on each trial = 0.4 qbinom(.10, size=10, prob=.4) # [1] 2 #find the 40th quantile of a binomial distribution with 30 trials and prob #of success on each trial = 0.25 qbinom(.40, size=30, prob=.25) # [1] 7

**rbinom**

The function **rbinom **generates a vector of binomial distributed random variables given a vector length *n*, number of trials (size) and probability of success on each trial (prob). The syntax for using rbinom is as follows:

**rbinom(n, size, prob) **

The following code illustrates a few examples of **rnorm** in action:

#generate a vector that shows the number of successes of 10 binomial experiments with #100 trials where the probability of success on each trial is 0.3. results #find mean number of successes in the 10 experiments (compared to expected #mean of 30) mean(results) # [1] 32.8 #generate a vector that shows the number of successes of 1000 binomial experiments #with 100 trials where the probability of success on each trial is 0.3. results #find mean number of successes in the 100 experiments (compared to expected #mean of 30) mean(results) # [1] 30.105

Notice how the more random variables we create, the closer the mean number of successes is to the expected number of successes.

*Note: “Expected number of successes” = n * p where n is the number of trials and p is the probability of success on each trial.*