Documentación

Esta página aún no se ha traducido para esta versión. Puede ver la versión más reciente de esta página en inglés.

# binopdf

Binomial probability density function

## Sintaxis

```Y = binopdf(X,N,P) ```

## Description

`Y = binopdf(X,N,P)` computes the binomial pdf at each of the values in `X` using the corresponding number of trials in `N` and probability of success for each trial in `P`. `Y`, `N`, and `P` can be vectors, matrices, or multidimensional arrays that all have the same size. A scalar input is expanded to a constant array with the same dimensions of the other inputs.

The parameters in `N` must be positive integers, and the values in `P` must lie on the interval [0, 1].

The binomial probability density function for a given value x and given pair of parameters n and p is

`$y=f\left(x|n,p\right)=\left(\begin{array}{c}n\\ x\end{array}\right){p}^{x}{q}^{\left(n-x\right)}{I}_{\left(0,1,...,n\right)}\left(x\right)$`

where q = 1 – p. The result, y, is the probability of observing x successes in n independent trials, where the probability of success in any given trial is p. The indicator function I(0,1,...,n)(x) ensures that x only adopts values of 0, 1, ..., n.

## Examples

A Quality Assurance inspector tests 200 circuit boards a day. If 2% of the boards have defects, what is the probability that the inspector will find no defective boards on any given day?

```binopdf(0,200,0.02) ans = 0.0176```

What is the most likely number of defective boards the inspector will find?

```defects=0:200; y = binopdf(defects,200,.02); [x,i]=max(y); defects(i) ans = 4```

Download ebook