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LogauaEngstrom
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statistical-analysis

Automate statistical analyses and APA-style reporting for research data.

18|1|Updated Dec 27, 2025
Search Tags:#regression#assumption-checking#hypothesis-testing#statistical-analysis#bayesian#power-analysis#apa-style
One-click install
npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill statistical-analysis-logauaengstrom
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/LogauaEngstrom/claude-scientific-skills/tree/main/scientific-skills/statistical-analysis
Command: npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill statistical-analysis-logauaengstrom

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, matplotlib, seaborn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Statistical analysis toolkit to streamline hypothesis testing, modeling, diagnostics, and APA reporting for academic research.

Core Features & Use Cases

  • Test selection guidance and planning for t-tests, ANOVA, chi-square, regression, and Bayesian tests
  • Automated assumption checks with diagnostic visuals and actionable recommendations
  • End-to-end analysis execution, effect size calculation, power analysis, and APA-style reporting for publication-ready results
  • Bayesian modeling and model comparison workflows with posterior summaries and diagnostics

Quick Start

Load your dataset, choose analysis options, and run the full pipeline to generate a ready-to-publish statistical report.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I automate APA-style statistical reporting for hypothesis testing in Python?▼

Automating APA-style statistical reporting is achieved by running analysis pipelines that execute t-tests, ANOVA, and regression to generate publication-ready results, effect sizes, and power analyses directly in Python.

What is the best way to run statistical assumption checks before regression analysis?▼

Statistical assumption checks are automated through diagnostic visuals and actionable recommendations, verifying data suitability for regression and ANOVA models before executing the core analysis pipeline.

Can I perform Bayesian modeling and model comparison using scipy and pandas?▼

Bayesian modeling and model comparison workflows are supported with posterior summaries and diagnostics, utilizing Python libraries alongside scipy and pandas for comprehensive statistical analysis.

Does this statistical analysis workflow calculate effect sizes and power analysis automatically?▼

Effect size calculation and power analysis are automatically computed during end-to-end analysis execution, providing comprehensive statistical diagnostics and APA-style reporting outputs for research data.

Do I need to manually format matplotlib and seaborn visuals for statistical diagnostics?▼

Manually formatting matplotlib and seaborn visuals is unnecessary because diagnostic visuals and actionable recommendations are automatically generated during the statistical analysis and assumption checking pipeline.

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