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So I’m going to try to explain what they are in this post.Recall that a random variable is a variable whose value is the outcome of a random event (see the first introductory post for a refresher if this doesn’t make any sense to you).The formula for the binomial distribution is; A function which is used to define the distribution of a probability is called a Probability distribution function.
For example, a random variable could be the outcome of the roll of a die or the flip of a coin.
To be explicit, this is an example of a discrete univariate probability distribution with finite support.
The probability distribution P(X) of a random variable X is the system of numbers.
In my first and second introductory posts I covered notation, fundamental laws of probability and axioms.
Let us discuss now both the type along with its definition and formula.
This is also known as a continuous or cumulative probability distribution.The possible result of a random experiment is called an outcome. With the help of these experiments or events, we can always create a probability pattern table in terms of variable and probabilities.There are basically two types of probability distribution which are used for different purposes and various types of data generation process.The probability distribution gives the possibility of each outcome of a random experiment or events.It provides the probabilities of different possible occurrence.Then the probability mass function f(X=x) = P () The table could be created on the basis of a random variable and possible outcomes.Say, a random variable X is a real-valued function whose domain is the sample space of a random experiment. X is the random variable of the number of heads obtained. These are the things that get mathematicians excited.However, probability theory is often useful in practice when we use probability distributions.Similarly, set of complex numbers, set of a prime number, set of whole numbers etc are the examples of Normal Probability distribution.Also, in real-life scenarios, the temperature of the day is an example of continuous probability.