Binomial Distributed Data Confidence Interval Calculation: Formulas, Algorithms and Examples
When collecting experimental data, the observable may be dichotomous. Sampling (eventually with replacement) thus emulates a Bernoulli trial leading to a binomial proportion. Because the binomial distribution is discrete, the analytical evaluation of the exact confidence interval of the sampled outc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/6/1104 |
| _version_ | 1797482090402414592 |
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| author | Lorentz Jäntschi |
| author_facet | Lorentz Jäntschi |
| author_sort | Lorentz Jäntschi |
| collection | DOAJ |
| description | When collecting experimental data, the observable may be dichotomous. Sampling (eventually with replacement) thus emulates a Bernoulli trial leading to a binomial proportion. Because the binomial distribution is discrete, the analytical evaluation of the exact confidence interval of the sampled outcome is a mathematical challenge. This paper proposes three alternative confidence interval calculation methods that are characterized and exemplified. |
| first_indexed | 2024-03-09T22:23:19Z |
| format | Article |
| id | doaj.art-a0fdbfc4c0444975b81fbdea6e716bd7 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T22:23:19Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-a0fdbfc4c0444975b81fbdea6e716bd72023-11-23T19:10:54ZengMDPI AGSymmetry2073-89942022-05-01146110410.3390/sym14061104Binomial Distributed Data Confidence Interval Calculation: Formulas, Algorithms and ExamplesLorentz Jäntschi0Department of Physics and Chemistry, Technical University of Cluj-Napoca, 400641 Cluj, RomaniaWhen collecting experimental data, the observable may be dichotomous. Sampling (eventually with replacement) thus emulates a Bernoulli trial leading to a binomial proportion. Because the binomial distribution is discrete, the analytical evaluation of the exact confidence interval of the sampled outcome is a mathematical challenge. This paper proposes three alternative confidence interval calculation methods that are characterized and exemplified.https://www.mdpi.com/2073-8994/14/6/1104binomial distributionbinomial proportionconfidence intervalexact methods |
| spellingShingle | Lorentz Jäntschi Binomial Distributed Data Confidence Interval Calculation: Formulas, Algorithms and Examples Symmetry binomial distribution binomial proportion confidence interval exact methods |
| title | Binomial Distributed Data Confidence Interval Calculation: Formulas, Algorithms and Examples |
| title_full | Binomial Distributed Data Confidence Interval Calculation: Formulas, Algorithms and Examples |
| title_fullStr | Binomial Distributed Data Confidence Interval Calculation: Formulas, Algorithms and Examples |
| title_full_unstemmed | Binomial Distributed Data Confidence Interval Calculation: Formulas, Algorithms and Examples |
| title_short | Binomial Distributed Data Confidence Interval Calculation: Formulas, Algorithms and Examples |
| title_sort | binomial distributed data confidence interval calculation formulas algorithms and examples |
| topic | binomial distribution binomial proportion confidence interval exact methods |
| url | https://www.mdpi.com/2073-8994/14/6/1104 |
| work_keys_str_mv | AT lorentzjantschi binomialdistributeddataconfidenceintervalcalculationformulasalgorithmsandexamples |