When we use binomial distribution and Poisson distributions?

data_science

#1

When we use binomial distribution and Poisson distributions.
Please explain with a example of real scenario.


#2

Just read about these distribution as a theory. Most sites have detail explanation with example.

Core of binomical and poisson will help you figure out where it can be applied in real scenario.


#3

I assume that you did read about these Distributions on different websites and that you come here to getting easier understanding.

A binomial experiment has the following characteristics:

  • The experiment consists of a fixed number of trials, n.
  • Each trial has two possible outcomes. For example, success or failure.
  • The probabilities of both outcomes are constant throughout the experiment.
  • Each trial in the experiment is independent.

Flipping a coin five times and recording the number of heads is one example of a binomial experiment. The number of trials is fixed (n = 5), the result of each coin flip is either heads or tails, the coin is just as likely to land heads on every toss, and each flip of the coin is unaffected by the other coin tosses.

A Poisson process has the following characteristics:

  • The experiment counts the number of times an event occurs over a specific period of measurements; such as time, area, or distance.
  • The mean of the Poisson distribution is the same for each interval of measurement.
  • The number of occurrences in each interval is independent.

An example of a Poisson process would be the number of cars that pass through a tollbooth during one hour.