# 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):
Result
Probability : P(X = x)
Mean : E(X)
Variance : V(X)
Standard Deviation :

## How to use Bernoulli Process Calculator?

Step 1 - Enter the Probability of success

Step 2 - Enter the number of success

Step 3 - Click Bernoulli Process Calculator button

Step 4 - Calculate mean of Bernoulli distribution

Step 5 - Calculate variance of Bernoulli distribution

Step 6 - Calculate standard deviation of Bernoulli distribution

## Bernoulli's Distribution Theory

A random experiment having two outcomes, viz., success or failure with respective probabilities $p$ and $q$ is called Bernoulli trial or Bernoulli experiment.

The probability distribution of the random variable $X$ representing the number of success obtained in a Bernoulli experiment is called Bernoulli distribution. Thus the random variable $X$ takes the value 0 and 1 with respective probabilities $q$ and $p$, i.e.,

 $$\begin{equation*} P(X=0) = q, \text{ and } P(X=1) = p. \end{equation*}$$

## Bernoulli's Distribution Definition

The discrete random variable $X$ is said to have Bernoulli distribution if its probability mass function (p.m.f.) is given by

 $$\begin{equation*} P(X=x) = p^x q^{1-x},\; x=0,1; 0 < p < 1; q=1-p. \end{equation*}$$

Here

• $P(X=x)\geq 0$ for all $x$
• $\sum_{x} P(X=x) = P(X=0) + P(X=1) = q+p =1$.

Hence $P(X=x)$ is a probability mass function (p.m.f.).

## Key features of Bernoulli's Distribution

• There are only two outcomes for a random experiment like success ($S$) and failure ($F$).
• The outcomes are mutually exclusive.
• The probability of success is $p$.
• The random variable $X$ is the total number of success.

## Mean of Bernoulli's Distribution

The mean of Bernoulli distribution is

 $$\begin{eqnarray*} \text{mean }= E(X) &=& \sum_{x=0}^1 x P(X=x) \\ &=& 0\times P(X=0) + 1\times P(X=1)\\ &=& 0\times q + 1\times p = p. \end{eqnarray*}$$

## Variance of Bernoulli's Distribution

To find variance of Bernoulli distribution $X$, we need to find $E(X^2)$.

 $$\begin{eqnarray*} E(X^2) &=& \sum_{x=0}^1 x^2 P(X=x) \\ &=& 0\times P(X=0) + 1\times P(X=1)\\ &=& 0\times q + 1\times p\\ &=& p. \end{eqnarray*}$$

Hence, the variance of Bernoulli distribution is

 $$\begin{eqnarray*} \text{ Variance }= V(X) &=& E(X^2)-[E(x)]^2\\ &=& p-p^2 \\ &=& p(1-p)\\ &=& pq. \end{eqnarray*}$$

Thus if $X$ is Bernoulli random variable with parameter $p$, then mean is $E(X)=p$ and variance is $V(X)= pq$.

Mean $>$ Variance.

## M.G.F. of Bernoulli Distribution

The m.g.f. of Bernoulli Distribution is given by

 $$\begin{eqnarray*} M_X(t) &=& E(e^{tX}) \\ &=& \sum_{x=0}^1 e^{tx}P(X=x)\\ &=& e^0 P(X=0) + e^tP(X=1)\\ &=& q+pe^t. \end{eqnarray*}$$

Thus, the moment generating function of Bernoulli's distribution is $M_X(t) = q+pe^t$.

## P.G.F. of Bernoulli Distribution

The p.g.f. of Bernoulli Distribution is given by

 $$\begin{eqnarray*} P_X(t) &=& E(t^{X}) \\ &=& \sum_{x=0}^1 t^xP(X=x)\\ &=& t^0 P(X=0) + t^1P(X=1)\\ &=& q+pt. \end{eqnarray*}$$

Thus, the probability generating function of Bernoulli's distribution is $P_X(t) = q+pt$.

## Conclusion

I hope you find above article on Bernoulli Distribution Calculator helpful and educational. 