# Mean

Hypergeometric distribution expected value.

Imagine a scenario with a population of size N, of which a subpopulation of size K can be considered successes. We draw n observations from the total population. Defining the random variable X as the number of successes in the n draws, X is said to follow a hypergeometric distribution. The mean for a hypergeometric random variable is

## Usage

var mean = require( '@stdlib/math/base/dists/hypergeometric/mean' );


#### mean( N, K, n )

Returns the expected value of a hypergeometric distribution with parameters N (population size), K (subpopulation size), and n (number of draws).

var v = mean( 16, 11, 4 );
// returns 2.75

v = mean( 2, 1, 1 );
// returns 0.5


If provided NaN as any argument, the function returns NaN.

var v = mean( NaN, 10, 4 );
// returns NaN

v = mean( 20, NaN, 4 );
// returns NaN

v = mean( 20, 10, NaN );
// returns NaN


If provided a population size N, subpopulation size K, or draws n which is not a nonnegative integer, the function returns NaN.

var v = mean( 10.5, 5, 2 );
// returns NaN

v = mean( 10, 1.5, 2 );
// returns NaN

v = mean( 10, 5, -2.0 );
// returns NaN


If the number of draws n or the subpopulation size K exceed population size N, the function returns NaN.

var v = mean( 10, 5, 12 );
// returns NaN

v = mean( 10, 12, 5 );
// returns NaN


## Examples

var randu = require( '@stdlib/random/base/randu' );
var round = require( '@stdlib/math/base/special/round' );
var mean = require( '@stdlib/math/base/dists/hypergeometric/mean' );

var v;
var i;
var N;
var K;
var n;

for ( i = 0; i < 10; i++ ) {
N = round( randu() * 20 );
K = round( randu() * N );
n = round( randu() * K );
v = mean( N, K, n );
console.log( 'N: %d, K: %d, n: %d, E(X;N,K,n): %d', N, K, n, v.toFixed( 4 ) );
}