statistical-analysis

Automate statistical analyses with hypothesis tests, regressions, and APA-style reporting.

2|Updated Oct 29, 2025

Search Tags:#statistics #regression #hypothesis-testing #bayesian #power-analysis #apa-reporting #assumption-checks

Ready to run

npx skills add https://github.com/shanelindsay/agentic-r --skill statistical-analysis-shanelindsay

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/shanelindsay/agentic-r/tree/main/skills/scientific-thinking/statistical-analysis
Command: npx skills add https://github.com/shanelindsay/agentic-r --skill statistical-analysis-shanelindsay

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Researchers often struggle to perform and report complex statistics, from basic tests to Bayesian analyses, with consistent diagnostics and reproducible outputs.

Core Features & Use Cases

Supports t-tests, ANOVA, regression, correlations, nonparametric alternatives, and Bayesian methods
Automates assumption checks, effect sizes, power analyses, and APA-style reporting
Use cases include experimental and observational data analyses with ready-to-publish templates

Quick Start

Run the included scripts on your dataset to generate a full statistical report and figures.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema

How do I automate hypothesis testing and assumption checks for academic research data?▼

Automating statistical analysis for academic research runs hypothesis tests, checks assumptions, calculates effect sizes, and generates ready-to-publish reports. You run included scripts on your dataset to produce full diagnostics and figures automatically.

What is the best way to generate APA-style reports with effect sizes and power analyses?▼

Generating APA-style reports requires combining test results, effect sizes, and power analyses into a standardized format. This workflow automates those calculations and applies APA-reporting templates to produce ready-to-publish outputs from your raw experimental or observational data.

Does this statistical analysis workflow support Bayesian methods and nonparametric alternatives?▼

Yes, the statistical analysis workflow supports Bayesian methods and nonparametric alternatives alongside t-tests, ANOVA, regression, and correlations, applying automated diagnostics and effect size calculations across all test types.

Can I use pandas and scipy datasets for comprehensive regression and correlation analysis?▼

Yes, you can use pandas and scipy datasets for regression and correlation analysis. The workflow processes dataframes to run comprehensive tests, apply automated assumption checks, and output formatted figures using matplotlib and seaborn.

What statistical tests are available for experimental and observational data analysis?▼

Statistical tests available for experimental and observational data include t-tests, ANOVA, regression, correlations, nonparametric alternatives, and Bayesian methods, all featuring automated effect size calculations and power analyses.

Why do I need automated assumption checks before running ANOVA or regression models?▼

Automated assumption checks validate model prerequisites before running ANOVA or regression, ensuring diagnostic integrity. They automatically verify data distribution and variance criteria to maintain reproducible, ready-to-publish statistical reporting.