## Continuous Uniform Distribution Calculator With Examples

Continuous Uniform Distribution Calculator With Examples The continuous uniform distribution is the simplest probability distribution where all the values belonging to its support have the same probability density. It is also known as rectangular distribution. This tutorial will help you understand how to solve the numerical examples based on continuous uniform distribution. Continuous Uniform Distribution …

## Poisson Distribution Calculator With Examples

Poisson Distribution Calculator Poisson distribution calculator helps you to determine the probability and cumulative probabilities for Poisson random variable given the mean number of successes ($\lambda$). Poisson Distribution Calculator Average rate of success ($\lambda$): Number of success (x): Calculate Result Probability : P(X = x) Cumulative Probability : P(X ≤ x) Cumulative Probability : P(X …

## Binomial Distribution Calculator with Step by Step Solution

Binomial distribution Calculator with Step by Step Binomial distribution is one of the most important discrete distribution in statistics. In this tutorial we will discuss about how to solve numerical examples based on binomial distribution. Upon successful completion of this tutorial, you will be able to understand how to calculate binomial probabilities. For the theoretical …

## Bernoulli Distribution Calculator

Bernoulli Distribution Calculator Bernoulli’s Process Calculator can help you to calculate the mean, variance and probability for Bernoulli’s distribution with parameter probability of success $p$. Bernoulli Process Calculator Probability of success (p): Number of success (x): Calculate Result Probability : P(X = x) Mean : E(X) Variance : V(X) Standard Deviation : How to use …

## Chebyshev’s Inequality

Chebyshev’s Inequality Chebyshev’s Inequality is a very powerful inequality, because it applies to any probability distribution. Chebyshev’s Inequality is used to estimate the probability that a random variable $X$ is within $k$ standard deviation of the mean. Before we derive Chebyshev’s Inequality, let us derive the Chebyshev’s Theorem. Chebyshev’s Theorem If $g(x)$ is a non-negative …

## Gamma Distribution Calculator with examples

Gamma distribution calculator with examples Use this calculator to find the probability density and cumulative probabilities for Gamma distribution with parameter $\alpha$ and $\beta$. Gamma Distribution Calculator Shape Parameter $\alpha$: Scale Parameter $\beta$ Value of x Calculate Results Probability density : f(x) Probability X less than x: P(X < x) Probability X greater than x: …

## Exponential Distribution Calculator with Examples

Exponential Distribution Calculator Exponential Distribution Calculator is used to find the probability density and cumulative probabilities for Exponential distribution with parameter $\theta$. Exponential Distribution Calculator Parameter $\theta$: Value of A Value of B Calculate Results Probability X less than A: P(X < A) Probability X greater than B: P(X > B) Probability X is between …

## Normal Distribution

Normal Distribution Normal distribution is one of the most fundamental distribution in Statistics. It is also known as Gaussian distribution. Definition of Normal Distribution A continuous random variable $X$ is said to have a normal distribution with parameters $\mu$ and $\sigma^2$ if its probability density function is given by  \begin{equation*} f(x;\mu, \sigma^2) = \left\{ …

## Normal approximation to Poisson distribution Examples

In this tutorial we will discuss some numerical examples on Poisson distribution where normal approximation is applicable. For large value of the $\lambda$ (mean of Poisson variate), the Poisson distribution can be well approximated by a normal distribution with the same mean and variance. Normal approximation to Poisson distribution Examples Let $X$ be a Poisson …

## Hypergeometric Distribution

Hypergeometric Distribution A hypergeometric experiment is an experiment which satisfies each of the following conditions: The population or set to be sampled consists of $N$ individuals, objects, or elements (a finite population). Each object can be characterized as a "defective" or "non-defective", and there are $M$ defectives in the population. A sample of $n$ individuals …