# Normal Distribution With Examples

In the previous two articles of this unit we learned Binomial distribution and Poisson Distribution, in this article we will learn Normal distribution.
Normal Distribution

# Poisson Distribution With Examples

Now we will learn Poisson distribution. In the previous article we learn Binomial distribution. So let’s start.
Poisson Distribution
Definition
Let X be a discrete

# Binomial Distribution With Examples

Binomial Distribution
Before starting our discussion on Binomial Distribution we have to understand what Bernoulli Trials is.
Bernoulli Trials
Trials are called Bernoulli trials if

# All About Moment Generating Function With Examples

Moment generating function
In this article we will first learn what a moment generating function (mgf) is and then we will learn how to use

# What is Covariance?

Covariance Definition
Covariance is a measure of association between two random variables. Let X and Y be two random variables. Then the covariance is defined

# Variance Formula of Random Variable With Example

Variance
One of the important measures of variability of a random variable is variance. Let X is a random variable with probability distribution f(x) and

# Mathematical Expectation of Random Variables With Examples And Expected Value Formula

Mathematical Expectation of Random Variables
In the last three articles of probability we studied about Random Variables of single and double variables, in this article

# Introduction To Cumulative Distribution Function, Marginal Probability And Joint Density Function

In this is article we are going to learn about the terms two dimensional random variable, cumulative distribution function, marginal probability and joint density function.

# Distribution Function Of Random Variables

Distribution Function Definition
Let X be an random variable, then the function such that F:R→R defined by F(X) = P(X ≤ x) is called distribution

# Random Variable And Its Distribution

Random Variable
By a random variable, we mean a real number x connected with the outcome of a random experiment. Random variables are of two