https://github.com/jabulente/effects-size-measurements
This repo implements scalable, reusable Python scripts to compute key effect size metrics—including Pearson’s r, Eta-squared, Partial Eta-squared, and Cohen’s d—to help quantify relationships and differences in data for statistical analysis.
https://github.com/jabulente/effects-size-measurements
cohens-d correlation-analysis data-analysis data-science effect-size-measurements effect-sizes matplotlib-pyplot numpy p-values pandas pearsonr python real-world-applications scipy-stats statistics statmodels
Last synced: 5 months ago
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This repo implements scalable, reusable Python scripts to compute key effect size metrics—including Pearson’s r, Eta-squared, Partial Eta-squared, and Cohen’s d—to help quantify relationships and differences in data for statistical analysis.
- Host: GitHub
- URL: https://github.com/jabulente/effects-size-measurements
- Owner: Jabulente
- Created: 2025-04-07T09:12:22.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-05-21T11:07:54.000Z (6 months ago)
- Last Synced: 2025-05-31T12:00:06.798Z (6 months ago)
- Topics: cohens-d, correlation-analysis, data-analysis, data-science, effect-size-measurements, effect-sizes, matplotlib-pyplot, numpy, p-values, pandas, pearsonr, python, real-world-applications, scipy-stats, statistics, statmodels
- Language: Jupyter Notebook
- Homepage:
- Size: 989 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md