{"id":19508616,"url":"https://github.com/danmadeira/algoritmos-estatistica-pl-sql","last_synced_at":"2026-06-11T02:31:35.164Z","repository":{"id":202124128,"uuid":"565467986","full_name":"danmadeira/algoritmos-estatistica-pl-sql","owner":"danmadeira","description":"Demonstração de Algoritmos de Estatística em PL/SQL","archived":false,"fork":false,"pushed_at":"2022-11-15T13:29:27.000Z","size":33,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-25T22:46:09.194Z","etag":null,"topics":["algorithms","data-analysis","data-science","database","oracle","oracle-database","pl-sql","statistics"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/danmadeira.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-11-13T14:06:38.000Z","updated_at":"2022-11-13T14:12:45.000Z","dependencies_parsed_at":null,"dependency_job_id":"db03c900-08d5-4d67-a6c7-8c50099d5437","html_url":"https://github.com/danmadeira/algoritmos-estatistica-pl-sql","commit_stats":null,"previous_names":["danmadeira/algoritmos-estatistica-pl-sql"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/danmadeira/algoritmos-estatistica-pl-sql","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danmadeira%2Falgoritmos-estatistica-pl-sql","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danmadeira%2Falgoritmos-estatistica-pl-sql/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danmadeira%2Falgoritmos-estatistica-pl-sql/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danmadeira%2Falgoritmos-estatistica-pl-sql/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/danmadeira","download_url":"https://codeload.github.com/danmadeira/algoritmos-estatistica-pl-sql/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danmadeira%2Falgoritmos-estatistica-pl-sql/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34180147,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-11T02:00:06.485Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["algorithms","data-analysis","data-science","database","oracle","oracle-database","pl-sql","statistics"],"created_at":"2024-11-10T23:08:23.910Z","updated_at":"2026-06-11T02:31:35.132Z","avatar_url":"https://github.com/danmadeira.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"## Demonstração de Algoritmos de Estatística em PL/SQL\n\nEsta é uma demonstração de funções que implementam alguns cálculos estatísticos. Os algoritmos que representam as fórmulas estatísticas estão escritos em uma estrutura simples e semelhante a aplicação pura das respectivas fórmulas, para deixar o código o mais limpo possível, e assim, didático.\n\nAté o momento, estas são algumas das fórmulas estatísticas que estão presentes:\n\n* Média Aritmética\n## $$\\mu = \\frac{1}{n} \\sum_{i=1}^{n} x_i$$\n\n* Média Aritmética Ponderada\n## $$\\mu_p = \\frac{1}{\\sum_{i=1}^{n} w_i} \\sum_{i=1}^{n} w_i x_i$$\n\n* Média Geométrica\n## $$\\mu_g = \\sqrt[n]{\\prod_{i=1}^{n} x_i}$$\n\n* Média Geométrica Ponderada\n## $$\\mu_{gp} = \\sqrt[\\sum_{i=1}^{n} w_i]{\\prod_{i=1}^{n} {x_i}^{w_i}}$$\n\n* Média Harmônica\n## $$\\mu_h = \\frac{n}{\\sum_{i=1}^{n} \\frac{1}{x_i}}$$\n\n* Média Harmônica Ponderada\n## $$\\mu_{hp} = \\frac{\\sum_{i=1}^{n} w_i}{\\sum_{i=1}^{n} \\frac{w_i}{x_i}}$$\n\n* Média Quadrática\n## $$\\mu_q = \\sqrt{\\frac{1}{n} \\sum_{i=1}^{n} {x_i}^2}$$\n\n* Média Quadrática Ponderada\n## $$\\mu_{qp} = \\sqrt{\\frac{1}{\\sum_{i=1}^{n} w_i} \\sum_{i=1}^{n} w_i{x_i}^2}$$\n\n* Média Cúbica\n## $$\\mu_c = \\sqrt[3]{\\frac{1}{n} \\sum_{i=1}^{n} {x_i}^3}$$\n\n* Média Cúbica Ponderada\n## $$\\mu_{cp} = \\sqrt[3]{\\frac{1}{\\sum_{i=1}^{n} w_i} \\sum_{i=1}^{n} w_i {x_i}^3}$$\n\n* Média Desarmônica\n## $$\\mu_d = \\frac{2}{\\frac{1}{\\frac{\\sum_{i=1}^{n} x_i}{n}} + \\frac{1}{\\frac{{\\bigl(\\frac{\\sum_{i=1}^{n} x_i}{n}\\bigl)}^2}{\\frac{n}{\\sum_{i=1}^{n} \\frac{1}{x_i}}}}}$$\n\n* Média Desarmônica Ponderada\n## $$\\mu_{dp} = \\frac{2}{\\frac{1}{\\frac{\\sum_{i=1}^{n} w_i x_i}{\\sum_{i=1}^{n} w_i}} + \\frac{1}{\\frac{{\\Bigl(\\frac{\\sum_{i=1}^{n} w_i x_i}{\\sum_{i=1}^{n} w_i}\\Bigl)}^2}{\\frac{\\sum_{i=1}^{n} w_i}{\\sum_{i=1}^{n} \\frac{w_i}{x_i}}}}}$$\n\n* Mediana\n\n* Moda\n\n* Desvio Absoluto Médio\n## $$D_{am} = \\frac{1}{n} \\sum_{i=1}^{n} |x_i - \\mu|$$\n\n* Desvio Absoluto Mediano\n## $$D_{am} = Md(|x_i - \\tilde{x}|)$$\n\n* Variância Populacional\n## $$\\sigma^2 = \\frac{1}{n} \\sum_{i=1}^{n} (x_i - \\mu)^2$$\n\n* Desvio Padrão Populacional\n## $$\\sigma = \\sqrt{\\frac{1}{n} \\sum_{i=1}^{n} (x_i - \\mu)^2}$$\n\n* Variância Amostral\n## $$s^2 = \\frac{1}{n-1} \\sum_{i=1}^{n} (x_i - \\bar{x})^2$$\n\n* Desvio Padrão Amostral\n## $$s = \\sqrt{\\frac{1}{n-1} \\sum_{i=1}^{n} (x_i - \\bar{x})^2}$$\n\n* Variância Populacional (para dados agrupados)\n## $$\\sigma^2 = \\frac{1}{\\sum_{i=1}^{n} w_i} \\sum_{i=1}^{n} \\bigl((x_i - \\mu)^2 w_i\\bigl)$$\n\n* Desvio Padrão Populacional (para dados agrupados)\n## $$\\sigma = \\sqrt{\\frac{1}{\\sum_{i=1}^{n} w_i} \\sum_{i=1}^{n} \\bigl((x_i - \\mu)^2 w_i\\bigl)}$$\n\n* Variância Amostral (para dados agrupados)\n## $$s^2 = \\frac{1}{\\sum_{i=1}^{n} w_i - 1} \\sum_{i=1}^{n} \\bigl((x_i - \\bar{x})^2 w_i\\bigl)$$\n\n* Desvio Padrão Amostral (para dados agrupados)\n## $$s = \\sqrt{\\frac{1}{\\sum_{i=1}^{n} w_i - 1} \\sum_{i=1}^{n} \\bigl((x_i - \\bar{x})^2 w_i\\bigl)}$$\n\n* Coeficiente de Variação\n## $$CV = \\frac{\\sigma}{\\mu} \\times 100$$\n\n* Coeficiente de Variação (para dados agrupados)\n## $$CV = \\frac{\\sigma}{\\mu} \\times 100$$\n\n* Covariância Populacional\n## $$\\sigma_{xy} = \\frac{1}{n} \\sum_{i=1}^{n} (x_i - \\mu_x)(y_i - \\mu_y)$$\n\n* Covariância Amostral\n## $$s_{xy} = \\frac{1}{n-1} \\sum_{i=1}^{n} (x_i - \\bar{x})(y_i - \\bar{y})$$\n\n* Coeficiente de Correlação Populacional de Pearson\n## $$\\rho_{xy} = \\frac{\\sigma_{xy}}{\\sigma_x \\sigma_y}$$\n\n* Coeficiente de Correlação Amostral de Pearson\n## $$r_{xy} = \\frac{s_{xy}}{s_x s_y}$$\n\n* Somatório dos Quadrados\n## $$SS_x = \\sum_{i=1}^{n} {x_i}^2 - \\frac{(\\sum_{i=1}^{n} x_i)^2}{n}$$\n\n* Somatório dos Produtos XY\n## $$SS_{xy} = \\sum_{i=1}^{n} x_i y_i - \\frac{(\\sum_{i=1}^{n} x_i)(\\sum_{i=1}^{n} y_i)}{n}$$\n\n* Coeficiente de Correlação de Pearson\n## $$r = \\frac{SS_{xy}}{\\sqrt{SS_x \\times SS_y}}$$\n\n* Z-score Populacional\n## $$z = \\frac{x - \\mu}{\\sigma}$$\n\n* Z-score Amostral\n## $$z = \\frac{x - \\bar{x}}{s}$$\n\n* Três Desvios\n\n* Amplitude\n\n* Assimetria\n## $$A = \\frac{1}{n} \\sum_{i=1}^{n} \\Bigl(\\frac{x_i - \\bar{x}}{s}\\Bigl)^3$$\n\n* Curtose\n## $$K = \\frac{1}{n} \\sum_{i=1}^{n} \\Bigl(\\frac{x_i - \\bar{x}}{s}\\Bigl)^4 - 3$$\n\n* Quartis\n## $$i = \\frac{j(n+1)}{4}$$\n## $$Q_j = x_i + \\biggl(\\frac{j(n+1)}{4} - i\\biggl) (x_{i+1} - x_i)$$\n*para j = 1, 2 e 3*\n\nAlém de funções para:\n\n* Desagrupar dados\n* Agrupar dados\n* Ordenar dados\n* Ordenar frequências\n\nObs.: As funções possuem versões para dados agrupados ou não agrupados e para dados amostrais ou populacionais. Há também um script de exemplo, com demonstrações das chamadas das funções.\n\n### Referências:\n\n- AGRESTI, A.; FRANKLIN, C.; KLINGENBERG, B. *Statistics: The Art and Science of Learning from Data*, 4th Edition. Pearson Education Limited, 2018.\n\n- ANDERSON, D. R.; SWEENEY, D. J.; WILLIAMS, T. A. *Essentials of Statistics for Business and Economics*, 5th Edition. Thomson South-Western, 2009.\n\n- ANDERSON, D. R.; SWEENEY, D. J.; WILLIAMS, T. A. *Statistics for Business and Economics*, 11th Edition. South-Western, Cengage Learning, 2011.\n\n- BALES, D. J. *Beginning Oracle PL/SQL*, 2nd Edition. Apress, 2015.\n\n- BLUMAN, A. G. *Elementary Statistics: A Step By Step Approach*, 10th Edition. McGraw-Hill, 2018.\n\n- BONAMENTE, M. *Statistics and Analysis of Scientific Data*, 2nd Edition. Springer Science Business Media, 2017.\n\n- BOSLAUGH, S.; WATTERS, P. A. *Statistics in a Nutshell*, 1st Edition. 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Thomson South-Western, 2008.\n\n- WITTE, R. S.; WITTE, J. S. *Statistics*, 11th Edition. John Wiley \u0026 Sons, 2017.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanmadeira%2Falgoritmos-estatistica-pl-sql","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanmadeira%2Falgoritmos-estatistica-pl-sql","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanmadeira%2Falgoritmos-estatistica-pl-sql/lists"}