mathematical-statistics

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.