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statistical-analysis

Guides statistical analysis with APA-style reporting using Python libraries like scipy and statsmodels.

1|Updated Mar 4, 2026
Search Tags:#analysis#statistics#assumptions#bayesian#power-analysis#test-selection#apa-reporting
One-click install
npx skills add https://github.com/Hung-3008/agusta --skill statistical-analysis-hung-3008
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/statistical-analysis
Command: npx skills add https://github.com/Hung-3008/agusta --skill statistical-analysis-hung-3008

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 often requires choosing the right tests, verifying assumptions, and producing APA-formatted results; this Skill guides users through that process, from test selection to reporting.

Core Features & Use Cases

It provides a structured workflow for test selection, assumption checking, running tests, interpreting results, reporting, and generating ready-to-publish outputs. It covers t-tests, ANOVA, regression, correlation, and Bayesian analyses, with automatic effect sizes, power analyses, and APA-style reporting. Use cases include evaluating group differences, relationships between variables, and predictive modeling in academic research.

Quick Start

Run an end-to-end statistical analysis: choose a test, check assumptions, execute the analysis, and generate an APA-style results report.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I run statistical analysis and generate an APA-formatted report automatically?▼

To run statistical analysis and generate an APA report, this Skill guides you through test selection, assumption checks, and execution using scipy and statsmodels, outputting ready-to-publish results with effect sizes and power analyses.

What statistical tests can I use for checking assumptions and evaluating group differences?▼

For checking assumptions and evaluating group differences, you can use t-tests, ANOVA, regression, and correlation analyses, with the workflow automatically verifying data assumptions before execution and reporting.

Does this statistical analysis workflow support Bayesian methods and power analysis?▼

Yes, the statistical analysis workflow supports Bayesian methods using pymc and arviz, alongside automatic power analyses and effect size calculations for academic research outputs.

Can I perform statistical analysis with Python libraries like numpy, pandas, and scipy?▼

Yes, you can perform statistical analysis using Python libraries like numpy, pandas, scipy, matplotlib, and seaborn, which are required dependencies for executing the analyses and generating reproducible outputs.

What is the best way to choose the right statistical test for my academic research data?▼

The best way to choose the right statistical test is using the Skill's structured test selection workflow, which matches your academic research data to appropriate tests, verifies assumptions, and interprets the results.

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