statistical-analyzer
Perform statistical hypothesis testing, regression analysis, ANOVA, and t-tests with plain-English interpretations and visualizations.
Profile →npx skills add majiayu000/claude-skill-registry --skill statistical-analyzer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Statistical Analyzer Guided statistical analysis with hypothesis testing, regression, ANOVA, and plain-English results. ## Features - **Hypothesis Testing**: t-tests, chi-square, proportion tests - **Regression Analysis**: Linear, polynomial, multiple regression - **ANOVA**: One-way, two-way ANOVA with post-hoc tests - **Distribution Analysis**: Normality tests, Q-Q plots - **Correlation Analysis**: Pearson, Spearman with significance - **Plain-English Results**: Interpret statistical outputs - **Visualizations**: Regression plots, residual analysis, box plots - **Report Generation**: PDF/HTML reports with interpretations ## Quick Start ```python from statistical_analyzer import StatisticalAnalyzer analyzer = StatisticalAnalyzer() # T-test analyzer.load_data(df, group_col='treatment', value_col='score') results = analyzer.t_test(group1='control', group2='experimental') print(results['interpretation']) # Regression analyzer.load_data(df) results = analyzer.linear_regression(x='age', y='income') print(f"R²: {results['r_squared']}") analyzer.plot_regression('regression.png') ``` ## CLI Usage ```bash # T-test python statistical_analyzer.py --data data.csv --test t-test --group treatm
- Features
- Quick Start
- CLI Usage
- API Reference
- StatisticalAnalyzer Class
- Tests
- T-Test
- ANOVA
- Regression Analysis
- Correlation Analysis
- Distribution Tests
- Interpretation Guide
- Significance Levels
- Effect Sizes
T-test python statistical_analyzer.py --data data.csv --test t-test --group treatment --value score --output results.html ANOVA python statistical_analyzer.py --data data.csv --test anova --group category --value score --output results.pdf Regression python statistical_analyzer.py --data data.csv --test regression --x age --y income --output report.pdf Correlation matrix python statistical_analyzer.py --data data.csv --test correlation --output correlation.png
What does the statistical-analyzer skill do?
Perform statistical hypothesis testing, regression analysis, ANOVA, and t-tests with plain-English interpretations and visualizations.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill statistical-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.