Random Numbers in R: Binomial Distribution

In summary, a binomial distribution in R is a probability distribution used to model the number of successes in a series of independent trials with two possible outcomes. To generate random numbers from a binomial distribution in R, the function rbinom(n, size, prob) is used. A binomial test in R is used to test the null hypothesis of equal probability of success in a binomial distribution. To visualize a binomial distribution in R, the functions dbinom(x, size, prob) and plot() can be used. Some common applications of binomial distributions in R include hypothesis testing, risk assessment, and modeling success rates in various industries.
  • #1
MaxManus
277
1
n is the population
p is the probability for one person to be sick
y is the numbers who are sick
I want to use R to generate random y. Binomial distribution. Does anyone know which command?
 
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  • #3
Yes, thanks for the help.
 

What is a binomial distribution in R?

A binomial distribution in R is a probability distribution that models the number of successes in a sequence of independent trials, where each trial has two possible outcomes (success or failure) and the probability of success remains constant across trials. It is used to model real-life scenarios such as flipping a coin or conducting surveys.

How do I generate random numbers from a binomial distribution in R?

In R, the function rbinom(n, size, prob) is used to generate n random numbers from a binomial distribution with size number of trials and prob probability of success. For example, rbinom(10, 5, 0.5) will generate 10 random numbers from a binomial distribution with 5 trials and a probability of success of 0.5.

What is the relationship between a binomial distribution and a binomial test in R?

A binomial test in R is used to test the null hypothesis that the probability of success in a binomial distribution is equal to a specified value. The test uses the binomial distribution to calculate the probability of obtaining a certain number of successes in a given number of trials, and then compares it to the significance level to determine if the null hypothesis can be rejected.

How can I visualize a binomial distribution in R?

You can use the dbinom(x, size, prob) function to plot the probability mass function of a binomial distribution with a specific size and prob. Additionally, you can use the plot() function to create a histogram of random numbers generated from a binomial distribution, providing a visual representation of the distribution.

What are some common applications of binomial distributions in R?

Binomial distributions in R are commonly used in various fields such as statistics, genetics, and finance. Some specific applications include hypothesis testing, risk assessment, and modeling the success rates of medical treatments or marketing campaigns.

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