{"id":19224412,"url":"https://github.com/hermann-web/some-common-statistical-methods","last_synced_at":"2026-06-26T12:30:15.147Z","repository":{"id":113472105,"uuid":"523086277","full_name":"Hermann-web/some-common-statistical-methods","owner":"Hermann-web","description":"python module, showcasing computation (as part of a learning process) of some common statistical methods including mininum sample size, confidence interval estimation methods for mean or proportion, hypothesis testing mehods and regression models witth metrics and test suites","archived":false,"fork":false,"pushed_at":"2024-04-08T01:24:46.000Z","size":596,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-04T23:35:24.879Z","etag":null,"topics":["adjusted-r-squared","beginner-project","confidence-interval","fisher-test","hypothesis-testing","kurtosis","linear-regression","log-likelihood","log-loss-score-metric","logistic-regression","metrics-evaluation","normality-test","p-value","python","statistical-analysis","statistical-models","statistics","student-test"],"latest_commit_sha":null,"homepage":"https://statanalysis.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Hermann-web.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-08-09T19:40:15.000Z","updated_at":"2024-04-07T15:36:34.000Z","dependencies_parsed_at":"2024-11-09T15:11:49.810Z","dependency_job_id":"e4399472-4f93-43b7-9605-d29102b1c7de","html_url":"https://github.com/Hermann-web/some-common-statistical-methods","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hermann-web%2Fsome-common-statistical-methods","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hermann-web%2Fsome-common-statistical-methods/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hermann-web%2Fsome-common-statistical-methods/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hermann-web%2Fsome-common-statistical-methods/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Hermann-web","download_url":"https://codeload.github.com/Hermann-web/some-common-statistical-methods/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240298487,"owners_count":19779283,"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":["adjusted-r-squared","beginner-project","confidence-interval","fisher-test","hypothesis-testing","kurtosis","linear-regression","log-likelihood","log-loss-score-metric","logistic-regression","metrics-evaluation","normality-test","p-value","python","statistical-analysis","statistical-models","statistics","student-test"],"created_at":"2024-11-09T15:11:37.479Z","updated_at":"2026-06-26T12:30:15.017Z","avatar_url":"https://github.com/Hermann-web.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [Statistical Analysis Toolkit](https://github.com/Hermann-web/some-common-statistical-methods)\n\nWelcome to [statanalysis](https://pypi.org/project/stat-analysis/), a repository of statistical methods and tools tailored for data analysis enthusiasts. Inspired by my completion of a Coursera certificate in statistics, this repository encompasses a plethora of statistical concepts meticulously crafted into implementations. From prediction metrics to regression analysis, hypothesis testing to confidence intervals, and population parameter estimation to model estimation, `statanalysis` covers it all.\n\nBuilt in Python, `statanalysis` provides meticulously crafted modules and utilities aimed at beginners in statistics, data science, and research. While following a certification on statistics on Coursera, I chose to solidify my knowledge through implementations instead of solely relying on existing modules. I believe there is no better way to understand a statistical formula than by implementing it in code, documenting it thoroughly, and validating the results through tests.\n\nSo, I've rewritten common statistical learning tools then create a [repository](https://github.com/Hermann-web/some-common-statistical-methods) that offers direct access to my implementations, ensuring simplicity without compromising accuracy. Futhermore, these implementations have undergone rigorous testing against established libraries like [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html), [statsmodels](https://www.statsmodels.org/stable/index.html), and [scikit-learn](https://scikit-learn.org/stable/modules/classes.html) to uphold industry standards.\n\nI have uploaded [this open source project](https://github.com/Hermann-web/some-common-statistical-methods) to pypi [python module available on pypi](https://pypi.org/project/stat-analysis/), documented on [readthedocs](https://statanalysis.readthedocs.io).\n\nWhether you're a novice or an experienced data analyst, `statanalysis` aims to simplify and enhance your statistical analysis journey. Dive in and explore a wealth of statistical methods and techniques designed to streamline your analytical processes and empower your insights.\n\n## Features\n\n1. **Utility Functions:**\n   - **Module:** `utils_md`\n   - **Description:** The `utils_md` module provides a collection of helper functions for various statistical tasks, including data preprocessing, standard deviation estimation, and computation of probabilities and percentiles.\n\n1. **Hypothesis Validation:**\n   - **Module:** `hyp_vali_md`\n   - **Description:** The `hyp_vali_md` module includes functions for hypothesis validation, such as checking residuals, coefficients, and conducting hypothesis tests. Features encompass:\n     - **Constraint Checking:** Functions for verifying constraints, such as checking if values fall within specific ranges.\n     - **Hypothesis Sample Size:** Tools for ensuring minimum sample sizes for hypothesis testing scenarios.\n\n1. **Confidence Interval Estimation:**\n   - **Module:** `conf_inte_md`\n   - **Description:** The `conf_inte_md` module offers methods for estimating confidence intervals for population parameters, such as proportions and means. Features include:\n     - **One-sample Proportion:** Functions for estimating confidence intervals for population proportions based on a single sample.\n     - **Two-sample Mean:** Methods for computing confidence intervals for the difference between two population means, considering paired and unpaired data.\n\n1. **Hypothesis Testing:**\n   - **Module:** `hyp_testi_md`\n   - **Description:** This module encompasses a comprehensive suite of functions for hypothesis testing, covering a variety of scenarios:\n     - **Testing Population Proportions:** Methods for assessing hypotheses related to population proportions using z-tests.\n     - **Comparing Means:** Functions for conducting hypothesis tests to compare means between two or more populations, employing t-tests and ANOVA.\n\n1. **Model Estimation:**\n   - **Module:** `mdl_esti_md`\n   - **Description:** The `mdl_esti_md` module houses classes and functions dedicated to model estimation. Notable features include:\n     - **Linear Regression:** Implementation of linear regression models, including ordinary least squares (OLS) and robust regression.\n     - **Logistic Regression:** Classes for logistic regression analysis, enabling binary classification tasks with probability predictions.\n     - **Multiple Regression:** Tools for conducting multiple regression analysis, facilitating the exploration of relationships between multiple independent variables and a dependent variable.\n\n## Repository Structure\n\nThe [repository](https://github.com/Hermann-web/some-common-statistical-methods) is organized into two main folders:\n\n1. **`statanalysis/` Folder:**\n\n   This folder contains the following modules:\n\n   - **`utils_md:`** Module for utility functions, offering a collection of helper functions for statistical tasks.\n   - **`hyp_vali_md:`** Module for hypothesis validation, containing functions for checking residuals, coefficients, and conducting hypothesis tests.\n   - **`conf_inte_md:`** Module for confidence interval estimation, providing methods for estimating confidence intervals for proportions and means.\n   - **`hyp_testi_md:`** Module for hypothesis testing, including functions for conducting hypothesis tests on proportions and means.\n   - **`mdl_esti_md:`** Module for model estimation, including classes and functions for linear regression, logistic regression, and multiple regression.\n\n2. **`tests/` Folder:**\n\n   This folder features tests for all methods mentioned above.\n\n## Usage\n\nTo utilize the statistical analysis functionalities provided by this library, you have either clone the repo or install from [pypi](https://pypi.org/project/stat-analysis/) depending on your usage\n\n### **Clone the Repository:**\n\nClone the repository to your local machine using the following command:\n\n```bash\ngit clone https://github.com/hermann-web/some-common-statistical-methods\n```\n\n### **Install the Library from PyPI:**\n\nInstall the library from [PyPI](https://pypi.org/project/stat-analysis/) using pip:\n\n```bash\npip install statanalysis\n```\n\nChoose the option that best suits your needs and get started with your statistical analysis.\n\n### **Import Modules or Functions:**\n\nIn your Python script, import the desired modules or functions using the following syntax:\n\n```python\nfrom statanalysis import utils_md, hyp_vali_md, conf_inte_md, hyp_testi_md, mdl_esti_md\n```\n\n### **Perform Statistical Analysis:**\n\nUtilize the imported functions and classes to perform a wide range of statistical analysis tasks on your data. For example:\n\n```python\n# Example: Compute a confidence interval for a population proportion\nconfidence_interval = conf_inte_md.IC_PROPORTION_ONE(sample_size=100, parameter=0.5, confidence=0.95)\n```\n\nLeverage advanced statistical techniques and methodologies provided by the modules to analyze your data effectively.\n\nAdditionally, if you prefer to browse documentation in a more structured format, you can refer to the online documentations which provides detailed information about the library's functionalities and usage.\n\n- [api reference on readthedocs](https://statanalysis.readthedocs.io/en/latest/statanalysis/statanalysis.html)\n- a [detailled onepager](https://github.com/Hermann-web/some-common-statistical-methods/blob/main/docs/detailled-docu.md)\n- a [more concice onepager](https://github.com/Hermann-web/some-common-statistical-methods/blob/main/docs/concise-docu.md)\n\n## Additional Information\n\n- The repository includes a comprehensive test suite in [tests](./tests/) folder to validate the accuracy and consistency of the implemented methods against standard industry-standard libraries like scipy.stats, statsmodels, and scikit-learn.\n- The [module is available on PyPI](https://pypi.org/project/stat-analysis/) for easy installation and use in various statistical analysis projects.\n- For detailed explanations and references, refer to the respective sections in the code files or [read the documentation on readthedocs](https://statanalysis.readthedocs.io/en/latest/statanalysis/statanalysis.html)\n- Further insights and explanations on statistical concepts can be found in the provided links.\n- For inquiries or assistance regarding the repository, please contact [Hermann Agossou](mailto:hermannagossou7[at]gmail.com).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhermann-web%2Fsome-common-statistical-methods","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhermann-web%2Fsome-common-statistical-methods","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhermann-web%2Fsome-common-statistical-methods/lists"}