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statistical-analysis

Perform hypothesis tests, regressions, and Bayesian modeling with APA-style reporting.

564|70|Updated Jan 25, 2026
Search Tags:#research#data analysis#hypothesis testing#statistics#regression#bayesian
One-click install
npx skills add https://github.com/ynulihao/AgentSkillOS --skill statistical-analysis-ynulihao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/ynulihao/AgentSkillOS/tree/main/data/skill_seeds/statistical-analysis
Command: npx skills add https://github.com/ynulihao/AgentSkillOS --skill statistical-analysis-ynulihao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, statsmodels, pingouin, pymc, arviz, matplotlib, pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for performing statistical analyses, from hypothesis testing to complex modeling, ensuring rigorous and interpretable research findings.

Core Features & Use Cases

  • Hypothesis Testing: Conduct t-tests, ANOVA, chi-square tests, and non-parametric alternatives.
  • Regression & Correlation: Perform linear, logistic, and Bayesian regression; calculate Pearson and Spearman correlations.
  • Assumption Checking & Reporting: Automate assumption verification and generate APA-style reports.
  • Use Case: A researcher needs to determine if a new teaching method significantly improves student test scores compared to the traditional method, while also checking for normality and homogeneity of variance assumptions.

Quick Start

Use the statistical-analysis skill to perform an independent samples t-test on 'group_a_scores.csv' and 'group_b_scores.csv'.

Frequently Asked Questions about statistical-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform hypothesis testing with automated assumption checking in Python?▼

Hypothesis testing with automated assumption checking is executed using Python libraries like scipy and pingouin to run t-tests, ANOVA, and chi-square tests while verifying normality and variance homogeneity.

Can I generate APA-style reports for regression and correlation analysis?▼

APA-style reports for regression and correlation analysis are generated automatically by the statistical-analysis toolkit, calculating effect sizes and power alongside linear, logistic, and Bayesian regression results.

Does this statistical analysis toolkit support Bayesian modeling with pymc?▼

Bayesian modeling is fully supported using pymc and arviz dependencies, enabling users to build complex Bayesian statistical models and interpret posterior distributions for academic research.

What's the best way to compare two groups of test scores for significant differences?▼

Comparing two groups of test scores is best handled by running an independent samples t-test on CSV data, which calculates effect size, checks assumptions, and determines statistical significance.

Do I need pandas and statsmodels to run non-parametric hypothesis tests?▼

Pandas and statsmodels are required dependencies for running non-parametric hypothesis tests, managing dataframes, and fitting statistical models to ensure rigorous and interpretable research findings.

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