@stdlib/stats
Version:
Standard library statistical functions.
99 lines (77 loc) • 2.56 kB
Plain Text
{{alias}}( [N, K, n] )
Returns a hypergeometric distribution object.
Parameters
----------
N: integer (optional)
Population size. Must be a nonnegative integer larger than or equal to
`K` and `n`.
K: integer (optional)
Subpopulation size. Must be a nonnegative integer smaller than or equal
to `N`.
n: integer (optional)
Number of draws. Must be a nonnegative integer smaller than or equal to
`N`.
Returns
-------
hypergeometric: Object
Distribution instance.
hypergeometric.N: number
Population size. If set, the value must be a nonnegative integer larger
than or equal to `K` and `n`.
hypergeometric.K: number
Subpopulation size. If set, the value must be a nonnegative integer
smaller than or equal to `N`.
hypergeometric.n: number
Number of draws. If set, the value must be a nonnegative integer
smaller than or equal to `N`.
hypergeometric.kurtosis: number
Read-only property which returns the excess kurtosis.
hypergeometric.mean: number
Read-only property which returns the expected value.
hypergeometric.mode: number
Read-only property which returns the mode.
hypergeometric.skewness: number
Read-only property which returns the skewness.
hypergeometric.stdev: number
Read-only property which returns the standard deviation.
hypergeometric.variance: number
Read-only property which returns the variance.
hypergeometric.cdf: Function
Evaluates the cumulative distribution function (CDF).
hypergeometric.logpmf: Function
Evaluates the natural logarithm of the probability mass function (PMF).
hypergeometric.pmf: Function
Evaluates the probability mass function (PMF).
hypergeometric.quantile: Function
Evaluates the quantile function at probability `p`.
Examples
--------
> var hypergeometric = {{alias}}( 100, 70, 20 );
> hypergeometric.N
100.0
> hypergeometric.K
70.0
> hypergeometric.n
20.0
> hypergeometric.kurtosis
~-0.063
> hypergeometric.mean
14.0
> hypergeometric.mode
14.0
> hypergeometric.skewness
~-0.133
> hypergeometric.stdev
~1.842
> hypergeometric.variance
~3.394
> hypergeometric.cdf( 2.9 )
~0.0
> hypergeometric.logpmf( 10 )
~-3.806
> hypergeometric.pmf( 10 )
~0.022
> hypergeometric.quantile( 0.8 )
16.0
See Also
--------