{"id":19508638,"url":"https://github.com/danmadeira/algoritmos-estatistica-python","last_synced_at":"2026-05-08T06:13:26.513Z","repository":{"id":202124108,"uuid":"477372353","full_name":"danmadeira/algoritmos-estatistica-python","owner":"danmadeira","description":"Demonstração de Algoritmos de Estatística em Python","archived":false,"fork":false,"pushed_at":"2022-04-03T14:52:06.000Z","size":128,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-08T11:40:38.145Z","etag":null,"topics":["algorithms","data-analysis","data-science","python","statistics"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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":"2022-04-03T14:50:38.000Z","updated_at":"2022-04-03T14:55:01.000Z","dependencies_parsed_at":null,"dependency_job_id":"2e4664cb-83e1-4df5-8681-a2d5e4f91281","html_url":"https://github.com/danmadeira/algoritmos-estatistica-python","commit_stats":null,"previous_names":["danmadeira/algoritmos-estatistica-python"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danmadeira%2Falgoritmos-estatistica-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danmadeira%2Falgoritmos-estatistica-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danmadeira%2Falgoritmos-estatistica-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danmadeira%2Falgoritmos-estatistica-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/danmadeira","download_url":"https://codeload.github.com/danmadeira/algoritmos-estatistica-python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240761091,"owners_count":19853254,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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","python","statistics"],"created_at":"2024-11-10T23:08:29.656Z","updated_at":"2026-05-08T06:13:21.489Z","avatar_url":"https://github.com/danmadeira.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Demonstração de Algoritmos de Estatística em Python\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* Média Aritmética Ponderada\n* Média Geométrica\n* Média Geométrica Ponderada\n* Média Harmônica\n* Média Harmônica Ponderada\n* Média Quadrática\n* Média Quadrática Ponderada\n* Média Cúbica\n* Média Cúbica Ponderada\n* Média Desarmônica\n* Média Desarmônica Ponderada\n* Mediana\n* Moda\n* Desvio Absoluto Médio\n* Desvio Absoluto Mediano\n* Variância Populacional\n* Desvio Padrão Populacional\n* Variância Amostral\n* Desvio Padrão Amostral\n* Coeficiente de Variação\n* Covariância Populacional\n* Covariância Amostral\n* Coeficiente de Correlação Populacional de Pearson\n* Coeficiente de Correlação Amostral de Pearson\n* Somatório dos Quadrados\n* Somatório dos Produtos XY\n* Z-score Populacional\n* Z-score Amostral\n* Três Desvios\n* Amplitude\n* Assimetria\n* Curtose\n* Quartis\n* Desagrupar dados\n* Agrupar dados\n\nObs.: As funções possuem versões para dados agrupados ou não agrupados e para dados amostrais ou populacionais. Ao final, há uma demonstração da execução das funções e, em alguns casos, há um comparativo com as funções disponíveis no Python.\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- 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. O'Reilly, 2008.\n\n- BRUCE, P.; BRUCE, A.; GEDECK, P. *Practical Statistics for Data Scientists*, 2nd Edition. O'Reilly Media, 2020.\n\n- DANGETI, P. *Statistics for Machine Learning*. Packt Publishing, 2017.\n\n- DEITEL, P.; DEITEL, H *Intro to Python: for Computer Science and Data Science*, Global Edition. Pearson Education Limited, 2022.\n\n- DEVORE, J. L. *Probability and Statistics for Engineering and the Sciences*, 9th Edition. Cengage Learning, 2016.\n\n- EMC Education Services. *Data Science \u0026 Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data*. John Wiley \u0026 Sons, 2015.\n\n- FORSYTH, D. *Probability and Statistics for Computer Science*. Springer International Publishing, 2018.\n\n- FREEDMAN, D.; PISANI, R.; PURVES. R. *Statistics*, 4th Edition. W. W. Norton \u0026 Company, 2007.\n\n- FREUND, R. J.; WILSON, W. J. *Statistical Methods*, 2nd Edition. Academic Press, 2003.\n\n- GRUS, J. *Data Science from Scratch: First Principles with Python*, Second Edition. O'Reilly Media, 2019.\n\n- HEUMANN, C.; SCHOMAKER, M.; SHALABH. *Introduction to Statistics and Data Analysis*. Springer International Publishing Switzerland, 2016.\n\n- HOGG, R. V.; MCKEAN, J. W.; CRAIG, A. T. *Introduction to Mathematical Statistics*, 8th Edition. Pearson Education, 2019.\n\n- HUBER, P. J.; RONCHETTI, E. M. *Robust Statistics*, Second Edition. John Wiley \u0026 Sons, 2009.\n\n- JOHNSON, R.; KUBY, P. *Elementary Statistics*, 11th edition. Brooks/Cole, Cengage Learning, 2012.\n\n- KOTZ, S.; READ, C. B.; BALAKRISHNAN, N.; VIDAKOVIC, B. *Encyclopedia of Statistical Sciences*, 2nd Edition, Vol 1-16. John Wiley \u0026 Sons, 2005.\n\n- MARTIN, B. R. *Statistics for Physical Sciences, An Introduction*, 1st Edition. Elsevier, 2012.\n\n- MENDENHALL, W.; BEAVER, R. J.; BEAVER, B. M. *Introduction to Probability and Statistics*, 13th Edition. Brooks/Cole, Cengage Learning, 2009.\n\n- MENDENHALL, W. M.; SINCICH, T. L. *Statistics for Engineering and the Sciences*, 6th Edition. CRC Press, Taylor \u0026 Francis Group, 2016.\n\n- MONTGOMERY, D. C.; RUNGER, G. C. *Applied Statistics and Probability for Engineers*, 3rd Edition. John Wiley \u0026 Sons, 2003.\n\n- NELLI, F. *Python Data Analytics: With Pandas, NumPy, and Matplotlib*, Second Edition. Apress Media LLC, 2018.\n\n- NISBET, R.; MINER, G.; YALE, K. *Handbook of Statistical Analysis and Data Mining Applications*, 2nd Edition. Academic Press, Elsevier, 2018.\n\n- OZDEMIR, S. *Principles of Data Science: Learn the techniques and math you need to start making sense of your data*. Packt Publishing, 2016.\n\n- PECK, R.; DEVORE, J. L. *Statistics: The Exploration and Analysis of Data*, 7th Edition. Brooks/Cole, Cengage Learning, 2012.\n\n- PECK, R.; OLSEN, C.; DEVORE, J. L. *Introduction to Statistics and Data Analysis*, 4th Edition. Brooks/Cole, Cengage Learning, 2012.\n\n- PROVOST, F.; FAWCETT, T. *Data Science for Business*, 1st Edition. O'Reilly Media, 2013.\n\n- RASCH, D.; SCHOTT, D. *Mathematical Statistics*. John Wiley \u0026 Sons, 2018.\n\n- RUPPERT, D.; MATTESON, D. S. *Statistics and Data Analysis for Financial Engineering with R examples*, Second Edition. Springer Texts in Statistics. Springer, 2015.\n\n- SALKIND, N. J. *Encyclopedia of Measurement and Statistics*, Vol 1-3. SAGE Publications, 2007.\n\n- SKIENA, S. S. *The Data Science Design Manual*. Texts in Computer Science. Springer International Publishing, 2017.\n\n- SPIEGEL, M. R.; STEPHENS, L. J. *Schaum's Outline of Statistics*, 6th Edition. McGraw-Hill Education, 2018.\n\n- WALPOLE, R. E.; MYERS, R. H.; MYERS, S. L.; YE, K. *Probability \u0026 Statistics for Engineers \u0026 Scientists*, 9th Edition, Global Edition. Pearson Education Limited, 2016.\n\n- WEIERS, R. M. *Introduction to Business Statistics*, 6th Edition. 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-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanmadeira%2Falgoritmos-estatistica-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanmadeira%2Falgoritmos-estatistica-python/lists"}