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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.
In this distribution, the set of possible outcomes can take on values on a continuous range.
For example, a set of real numbers, is a continuous or normal distribution, as it gives all the possible outcomes of real numbers.
Related Concepts: Probability distribution yields the possible outcomes for any random event.
It is also defined on the basis of underlying sample space as a set of possible outcomes of any random experiment.
The probability distribution gives the possibility of each outcome of a random experiment or events.
It provides the probabilities of different possible occurrence.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 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.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.To recall, probability is a measure of uncertainty of various phenomenon.Like, if you throw a dice, what is the possible outcomes of it, is defined by the probability.