broadwick.statistics.distributions

Class HypergeometricDistribution

java.lang.Object

broadwick.statistics.distributions.HypergeometricDistribution

All Implemented Interfaces:

DiscreteDistribution

public class HypergeometricDistribution
extends Object
implements DiscreteDistribution

The hypergeometric distribution applies to sampling without replacement from a finite population whose elements can be classified into two mutually exclusive categories like Pass/Fail. As random selections are made from the population, each subsequent draw decreases the population causing the probability of success to change with each draw.

See Also:

Hypergeometric distribution (Wikipedia), Hypergeometric distribution (MathWorld)

Field Summary

Fields

Modifier and Type	Field and Description
private org.apache.commons.math3.distribution.HypergeometricDistribution	dist

Constructor Summary

Constructors

Constructor and Description
HypergeometricDistribution(int popSize, int sampleSize, int numSuccesses) A hypergeometric distribution gives the distribution of the number of successes in \(n\) draws from a population of size \(N\) containing \(n_{succ}\) successes.

Method Summary

Modifier and Type	Method and Description
double	getMean() Get the numerical value of the mean of this distribution.
double	getVariance() Get the numerical value of the variance of this distribution.
int	sample() Generate a random value sampled from this distribution.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

dist

private final org.apache.commons.math3.distribution.HypergeometricDistribution dist

Constructor Detail

HypergeometricDistribution

public HypergeometricDistribution(int popSize,
                                  int sampleSize,
                                  int numSuccesses)

A hypergeometric distribution gives the distribution of the number of successes in \(n\) draws from a population of size \(N\) containing \(n_{succ}\) successes. \(N,n,n_{succ}\) can be any integers such that \(N\ge n>0\) and \(N\ge n_{succ} \ge 0\).

Parameters:

popSize - the population size, \(N\).

sampleSize - the number of draws, \(n\).

numSuccesses - the number of successes, \(n_{succ}\).

Method Detail

sample

public int sample()

Description copied from interface: DiscreteDistribution

Generate a random value sampled from this distribution.

Specified by:

sample in interface DiscreteDistribution

Returns:

the sampled value from the distribution.

getMean

public final double getMean()

Get the numerical value of the mean of this distribution.

For population size N, number of successes m, and sample size n, the mean is n * m / N.

Specified by:

getMean in interface DiscreteDistribution

Returns:

getVariance

public final double getVariance()

Get the numerical value of the variance of this distribution.

For population size N, number of successes m, and sample size n, the variance is [n * m * (N - n) * (N - m)] / [N^2 * (N - 1)].

Specified by:

getVariance in interface DiscreteDistribution

Returns: