stat-modeling-tools

What problem does it solve?

This Skill streamlines the process of performing statistical analyses on scientific datasets, ensuring reproducible results and clear reporting of findings.

Core Features & Use Cases

• Hypothesis Testing: Select and run appropriate statistical tests (e.g., t-tests, chi-square, correlation) on tabular data.
• Regression Analysis: Fit common statistical models like OLS, logistic, and Poisson regression using the statsmodels library.
• Reproducible Summaries: Generate machine-readable CSV and JSON outputs for statistical tests and regression models, suitable for manuscripts and reports.
• Use Case: Analyze experimental results by performing an independent t-test to compare two groups, reporting effect sizes and p-values, and saving the summary to a JSON file.

Quick Start

Run an independent t-test on the 'assay.csv' file, comparing the 'response' column between 'control' and 'treated' arms, saving the output to 'assay_ttest.csv' and 'assay_ttest.json'.