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On Discrete Probability Distributions to Grasp the Number of Samples in a Population

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  • Yabu, Takuya

Abstract

In situations where there are multiple options, when the number of options selected is known, the discrete probability distribution of the number of options selected is mathematically defined, and properties such as expected value and variance are shown. By using the discrete probability distributions derived in this paper, we can find the most probable number of people trying to buy and the most probable number of sales of other people's products when we know the number of sales of our own product. Therefore, application in the field of economics can be expected.

Suggested Citation

  • Yabu, Takuya, 2023. "On Discrete Probability Distributions to Grasp the Number of Samples in a Population," OSF Preprints yv24f_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:yv24f_v1
    DOI: 10.31219/osf.io/yv24f_v1
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    References listed on IDEAS

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    1. Linda J. Young & Michael Jacobsen, 2022. "Sample Design and Estimation When Using a Web-Scraped List Frame and Capture-Recapture Methods," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 261-279, June.
    2. Conti, Pier Luigi & Mecatti, Fulvia & Nicolussi, Federica, 2022. "Efficient unequal probability resampling from finite populations," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    3. Martin, Ian W.R. & Nagel, Stefan, 2022. "Market efficiency in the age of big data," Journal of Financial Economics, Elsevier, vol. 145(1), pages 154-177.
    4. Petersen, Alexander & Zhang, Chao & Kokoszka, Piotr, 2022. "Modeling Probability Density Functions as Data Objects," Econometrics and Statistics, Elsevier, vol. 21(C), pages 159-178.
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