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mathematical-statistics

Compute descriptive and inferential statistics for social science datasets.

24|7|Updated Nov 15, 2025
Search Tags:#python#statistics#regression#data-analysis#hypothesis-testing#factor-analysis#social-sciences
One-click install
npx skills add https://github.com/ptreezh/sscisubagent-skills --skill mathematical-statistics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mathematical-statistics
Source: https://github.com/ptreezh/sscisubagent-skills/tree/main/archive/skills/mathematical-statistics
Command: npx skills add https://github.com/ptreezh/sscisubagent-skills --skill mathematical-statistics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, numpy, pandas, statsmodels, pingouin, scikit-learn, factor-analyzer, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you perform rigorous, end-to-end statistical analysis for social science research by computing common descriptive metrics, running inferential tests, fitting core regression models, and supporting factor/measurement interpretation with quality checks.

Core Features & Use Cases

  • Descriptive statistics: central tendency, dispersion, distribution shape (e.g., skewness/kurtosis), plus publication-style reporting fields and charts-oriented summaries.
  • Inferential statistics: hypothesis testing such as t-tests and chi-square tests, including effect size and confidence-interval-oriented outputs where supported.
  • Regression analysis: simple linear regression, with support for more advanced regression workflows when scientific packages are available.
  • Variance analysis & factor/scale support (where available): one-way/multi-way variance concepts and exploratory factor analysis interfaces.
  • Research reporting workflow: produces structured JSON outputs suitable for academic write-ups, table generation, and reproducible analysis pipelines.

Quick Start

Use the mathematical-statistics skill to analyze your dataset by running descriptive statistics for a specified numeric variable column from a JSON input file.

Frequently Asked Questions about mathematical-statistics

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

FAQPage Schema
How do I run hypothesis testing and regression analysis for social science data in Python?▼

Hypothesis testing and regression analysis for social science data require computing descriptive and inferential statistics using pandas and statsmodels. This Skill performs t-tests, chi-square tests, and linear regression, returning structured JSON outputs for academic write-ups.

What's the best way to compute descriptive statistics and effect sizes for a research dataset?▼

Computing descriptive statistics and effect sizes involves measuring central tendency, dispersion, and distribution shape like skewness and kurtosis. This Skill calculates these metrics alongside inferential statistics with confidence intervals, generating publication-ready summaries.

Can I perform exploratory factor analysis and variance analysis using scipy and pandas?▼

Exploratory factor analysis and variance analysis are supported using scipy, pandas, and the factor-analyzer library. This Skill provides interfaces for one-way and multi-way variance concepts, plus factor and scale measurement interpretation with quality checks.

Does this statistical analysis workflow support JSON data interchange for reproducible pipelines?▼

JSON data interchange is fully supported for reproducible statistical analysis pipelines. This Skill requires numeric input columns from a JSON input file and produces structured JSON outputs suitable for academic table generation and research reporting workflows.

How do I conduct t-tests and chi-square tests with confidence intervals using Python?▼

Conducting t-tests and chi-square tests with confidence intervals relies on scipy and pingouin for enhanced results. This Skill executes these inferential statistics tests on social science datasets, outputting effect sizes and confidence intervals in a structured format.

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