statistical-analysis

Name: statistical-analysis
Availability: InStock
Author: jiaxiaojun

Perform statistical analyses on academic datasets with Python libraries.

75|7|Updated Feb 14, 2026

Search Tags:#statistics #data-analysis #hypothesis-testing #effect-size #power-analysis #apa-reporting #bayesian-statistics

Ready to run

npx skills add https://github.com/jiaxiaojunQAQ/SkillJect --skill statistical-analysis-jiaxiaojunqaq

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/jiaxiaojunQAQ/SkillJect/tree/main/data/skills_sample/statistical-analysis
Command: npx skills add https://github.com/jiaxiaojunQAQ/SkillJect --skill statistical-analysis-jiaxiaojunqaq

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Statistical analysis toolkit that enables rigorous hypothesis testing, regression modeling, and APA-style reporting for academic research, streamlining complex analyses into reproducible workflows.

Core Features & Use Cases

Test selection and planning, assumption checking, and execution of t-tests, ANOVA, regression, and Bayesian analyses.
Generation of professional reports with effect sizes, confidence intervals, and diagnostics to supports scholarly communication.
End-to-end guidance for power analyses, diagnostics, and robust interpretation across psychology, education, and social sciences.

Quick Start

Install dependencies and run a sample analysis using the provided scripts to generate a complete 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 perform hypothesis testing and generate an APA-style report for my academic dataset?▼

Hypothesis testing with APA-style reporting is executed through Python scripts that handle test selection, assumption checks, effect sizes, and power analysis to produce professional scholarly outputs.

Can I run Bayesian statistics and regression modeling using pandas and scipy?▼

Yes, Bayesian statistics and regression modeling are supported using pandas and scipy alongside statsmodels and pymc to extract rigorous conclusions from academic datasets.

What is the best way to compute effect sizes and power analysis for psychology research?▼

Effect sizes and power analysis for psychology research are computed through structured workflows that enforce assumption checks and comprehensive diagnostics, yielding robust statistical interpretations.

Does this statistical analysis workflow handle assumption checks before running t-tests and ANOVA?▼

Yes, the statistical analysis workflow explicitly enforces assumption checks before executing t-tests and ANOVA, ensuring rigorous hypothesis testing and valid conclusions.

Why do I need numpy and statsmodels for academic data analysis?▼

Numpy and statsmodels provide the foundational computational frameworks required for regression modeling, Bayesian analysis, and rigorous statistical diagnostics across social science datasets.