"hypergeometric random variable" meaning in English

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          "ref": "1992, Norman Lloyd Johnson, Samuel Kotz, Adrienne W. Kemp, Univariate Discrete Distributions, page 67:",
          "text": "Computer generation of classical hypergeometric random variables has been discussed in detail by Kachitvichyanukul and Schmeiser (1985).",
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          "ref": "2005, Martin Buntinas, Gerald Marlowe Funk, Statistics for the Sciences, page xv:",
          "text": "Chapter 6 introduces testing of hypotheses immediately after the study of binomial and hypergeometric random variables.",
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          "ref": "2007, Purna Chandra Biswal, Probability and Statistics, India: Prentice-Hall, published 2008, page 87:",
          "text": "If #x5C;textstyleX is a hypergeometric random variable, then the variance is #x5C;textstyleVar(X)#x3D;#x5C;left(#x5C;frac#x7B;M#x7D;#x7B;N#x7D;#x5C;right)n#x5C;left(1-#x5C;frac#x7B;M#x7D;#x7B;N#x7D;#x5C;right)#x5C;left(1-#x5C;frac#x7B;n-1#x7D;#x7B;N-1#x7D;#x5C;right).",
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          "ref": "2017, Sheldon M. Ross, Introductory Statistics, Elsevier (Academic Press), page 250:",
          "text": "Thus, whereas the expected value of the hypergeometric random variable with parameters n, N, p is the same as that of the binomial random variable with parameters n, p, its variance is smaller than that of the binomial by the factor (N − n)/(N − 1).",
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        "A random variable whose probability distribution is a hypergeometric distribution."
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        "(probability theory, statistics) A random variable whose probability distribution is a hypergeometric distribution."
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          "ref": "2007, Purna Chandra Biswal, Probability and Statistics, India: Prentice-Hall, published 2008, page 87:",
          "text": "If #x5C;textstyleX is a hypergeometric random variable, then the variance is #x5C;textstyleVar(X)#x3D;#x5C;left(#x5C;frac#x7B;M#x7D;#x7B;N#x7D;#x5C;right)n#x5C;left(1-#x5C;frac#x7B;M#x7D;#x7B;N#x7D;#x5C;right)#x5C;left(1-#x5C;frac#x7B;n-1#x7D;#x7B;N-1#x7D;#x5C;right).",
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          "text": "Thus, whereas the expected value of the hypergeometric random variable with parameters n, N, p is the same as that of the binomial random variable with parameters n, p, its variance is smaller than that of the binomial by the factor (N − n)/(N − 1).",
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