Statistics

Discrete Probability Distributions with R

Probability distributions describe the random process of events

Photo by Mohammad Rahmani on Unsplash

Discrete Probability Distributions

Before moving to the topic in detail, we must know what exactly probability distribution means?

In simpler terms, the probability distributions describe the random process (any phenomenon) in terms of probabilities.

What exactly random process is?

A random event is a random process to which we can never find the exact value or exact probability. The only way to proceed is to predict it.

For example, we can say,

• Tossing a coin (we do not know the outcome; it can be either Head or Tail).
• Drawing a card out of its deck (it can be any card from 52 cards).
• Describing any of the events in terms of probability is a Probability distribution

Probability distributions are of two types

1. Discrete
2. Continuous

Explanation — Discrete Probability Distributions

Here, we will be discussing some discrete distributions and how to use them. But first, let us come to the exact meaning of discrete distributions.

When the outcome of any random event is the discrete type that is countable, finite, non-negative integers, any number with infinite decimals, then the probability is projected or modeled with the help of discrete distributions.

A basic example of throwing a dice

X represents the value of a discrete random variable
P(X) represents the associated probability

Now the new thing that comes up is the Random variable. It is the possible quantitative value any outcome can take. Here X can take values 1, 2, 3, 4, 5, 6 because these are the only values that can come up when you throw a dice.

So, while working on we need to take care of two pointers.

Value any Random variable can take and their associated probabilities.

Conditions of a…