{"id":30065840,"url":"https://github.com/ricardorobledo/ml_statistical_methods","last_synced_at":"2026-04-11T12:33:41.496Z","repository":{"id":305054026,"uuid":"1021772165","full_name":"RicardoRobledo/ML_Statistical_methods","owner":"RicardoRobledo","description":null,"archived":false,"fork":false,"pushed_at":"2025-07-17T23:52:36.000Z","size":673,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-08T06:43:20.829Z","etag":null,"topics":["matplotlib","numpy","pandas","python3"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RicardoRobledo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2025-07-17T23:42:23.000Z","updated_at":"2025-07-17T23:52:39.000Z","dependencies_parsed_at":"2025-07-18T05:22:24.526Z","dependency_job_id":"918e648f-2357-49a9-aa2a-b38ded7b3def","html_url":"https://github.com/RicardoRobledo/ML_Statistical_methods","commit_stats":null,"previous_names":["ricardorobledo/ml_statistical_methods"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/RicardoRobledo/ML_Statistical_methods","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Statistical_methods","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Statistical_methods/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Statistical_methods/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Statistical_methods/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RicardoRobledo","download_url":"https://codeload.github.com/RicardoRobledo/ML_Statistical_methods/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Statistical_methods/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279009475,"owners_count":26084609,"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","status":"online","status_checked_at":"2025-10-11T02:00:06.511Z","response_time":55,"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":["matplotlib","numpy","pandas","python3"],"created_at":"2025-08-08T06:38:36.062Z","updated_at":"2025-10-12T00:13:33.184Z","avatar_url":"https://github.com/RicardoRobledo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Statistics Notebook\n\nThis notebook is based on Statistical methods for ML book available at [machinelearningmastery.com](https://machinelearningmastery.com/). It covers fundamental and advanced topics.\n\n## Key Topics Covered\n\n- **Gaussian distribution and summary statistics:** population vs sample, central tendency, variance  \n- **Simple data visualization:** Matplotlib basics, line plots, bar charts, histograms, box plots, scatter plots  \n- **Random numbers:** pseudorandom number generation, seeding, controlling randomness  \n- **Law of large numbers and Central Limit Theorem:** theory, examples, and implications in ML  \n- **Statistical hypothesis testing:** test interpretation, error types, degrees of freedom  \n- **Statistical distributions:** Gaussian, Student’s t, Chi-squared  \n- **Critical values and their use in tests**  \n- **Covariance and correlation:** Pearson’s correlation, test datasets  \n- **Significance tests:** parametric tests (t-test, ANOVA, repeated measures)  \n- **Effect size and statistical power:** importance, calculation, power analysis  \n- **Resampling methods:** statistical sampling, bootstrap, cross-validation  \n- **Estimation statistics:** problems with hypothesis testing, interval estimation, meta-analysis  \n- **Tolerance and confidence intervals:** calculation and interpretation  \n- **Prediction intervals:** calculation and worked examples  \n- **Nonparametric methods:** rank data, ranking, rank correlation (Spearman, Kendall), significance tests (Mann-Whitney U, Wilcoxon, Kruskal-Wallis, Friedman)  \n- **Independence tests:** contingency tables, Pearson’s Chi-squared test  \n\nThis notebook provides clear tutorials, practical examples, and worked problems to build a solid understanding of statistics essential for data scientists and ML practitioners.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fricardorobledo%2Fml_statistical_methods","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fricardorobledo%2Fml_statistical_methods","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fricardorobledo%2Fml_statistical_methods/lists"}