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The hypergeometric distribution is a discrete probability distribution.
Let n_1 be the number of objects of a class A and n_2 be the number of objects of class B. We take out n objects, without replacment. Then the hypergeometric distribution is the probability that exactly k objects are from class A. Of course n \leq n_1 + n_2.
Returns the value at x of the probability function of a \({\it Hypergeometric}(n1,n2,n)\)
random variable, with n_1, n_2 and n non negative integers and n\leq n_1+n_2. Being n_1 the number of objects of class A, n_2 the number of objects of class B, and n the size of the sample without replacement, this function returns the probability of event "exactly x objects are of class A".
To make use of this function, write first load("distrib").
The pdf is $$ f(x; n_1, n_2, n) = {\displaystyle{n_1\choose x} {n_2 \choose n-x} \over \displaystyle{n_2+n_1 \choose n}} $$
Returns the value at x of the distribution function of a \({\it Hypergeometric}(n1,n2,n)\)
random variable, with n_1, n_2 and n non negative
integers and n\leq n_1+n_2.
See pdf_hypergeometric for a more complete description.
To make use of this function, write first load("distrib").
The cdf is $$ F(x; n_1, n_2, n) = {n_2+n_1\choose n}^{-1} \sum_{k=0}^{\lfloor x \rfloor} {n_1 \choose k} {n_2 \choose n - k} $$
Returns the q-quantile of a
\({\it Hypergeometric}(n1,n2,n)\) random
variable, with n1, n2 and n non negative integers
and n\leq n1+n2; in other words, this is the inverse of cdf_hypergeometric. Argument q must be an element of [0,1]. To make use of this function, write first load("distrib").
Returns the mean of a discrete uniform random variable
\({\it Hypergeometric}(n_1,n_2,n)\), with n_1, n_2 and n non negative integers and n\leq n_1+n_2. To make use of this function, write first load("distrib").
The mean is $$ E[X] = {n n_1\over n_2+n_1} $$
Returns the variance of a hypergeometric random variable
\({\it Hypergeometric}(n_1,n_2,n)\), with n1, n2 and n non negative integers and n<=n1+n2. To make use of this function, write first load("distrib").
The variance is $$ V[X] = {n n_1 n_2 (n_1 + n_2 - n) \over (n_1 + n_2 - 1) (n_1 + n_2)^2} $$
Returns the standard deviation of a
\({\it Hypergeometric}(n_1,n_2,n)\) random variable, with n_1, n_2 and n non negative integers and n\leq n_1+n_2. To make use of this function, write first load("distrib").
The standard deviation is $$ D[X] = {1\over n_1+n_2}\sqrt{n n_1 n_2 (n_1 + n_2 - n) \over n_1+n_2-1} $$
Returns the skewness coefficient of a
\({\it Hypergeometric}(n1,n2,n)\) random variable, with n_1, n_2 and n non negative integers and n\leq n1+n2. To make use of this function, write first load("distrib").
The skewness coefficient is $$ SK[X] = {(n_2-n_2)(n_1+n_2-2n)\over n_1+n_2-2} \sqrt{n_1+n_2-1 \over n n_1 n_2 (n_1+n_2-n)} $$
Returns the kurtosis coefficient of a
\({\it Hypergeometric}(n_1,n_2,n)\) random variable, with n_1, n_2 and n non negative integers and n\leq n1+n2. To make use of this function, write first load("distrib").
The kurtosis coefficient is $$ \eqalign{ KU[X] = & \left[{C(1)C(0)^2 \over n n_1 n_2 C(3)C(2)C(n)}\right. \cr & \times \left.\left( {3n_1n_2\left((n-2)C(0)^2+6nC(n)-n^2C(0)\right) \over C(0)^2 } -6nC(n) + C(0)C(-1) \right)\right] \cr &-3 } $$
where \(C(k) = n_1+n_2-k\).
Returns a
\({\it Hypergeometric}(n1,n2,n)\) random variate, with n1, n2 and n non negative integers and n<=n1+n2. Calling random_hypergeometric with a fourth argument m, a random sample of size m will be simulated.
Algorithm described in Kachitvichyanukul, V., Schmeiser, B.W. (1985) Computer generation of hypergeometric random variates. Journal of Statistical Computation and Simulation 22, 127-145.
To make use of this function, write first load("distrib").