Statistics

This skill guides users through statistical hypothesis testing (t-tests, ANOVA, chi-square, regression) with proper assumption checking, effect size calculation, and APA-formatted reporting. It includes Python utilities for diagnostic visualizations and comprehensive reference documentation covering Bayesian methods, power analysis, and test selection.

A comprehensive reference skill for the statsmodels Python library, covering statistical modeling techniques including linear regression, generalized linear models, discrete choice models, time series analysis, and statistical diagnostics. Provides code examples, best practices, and detailed explanations for econometrics and rigorous statistical inference.

This skill provides comprehensive documentation and examples for using the seaborn Python library for statistical data visualization. It covers core plotting functions (scatter, line, distribution, categorical, regression, and matrix plots), the modern objects interface API, multi-plot grids, theming, and best practices for creating publication-quality figures.

A comprehensive Bayesian modeling skill built on PyMC 5.x that enables probabilistic programming with MCMC sampling (NUTS), variational inference, and hierarchical models. Includes diagnostic utilities for convergence checking, model comparison using LOO/WAIC, and templates for common model patterns like linear regression and multilevel models.

Guides users through planning and documenting A/B tests with statistical rigor. Includes hypothesis frameworks, sample size reference tables, test design templates, and analysis checklists to ensure valid experimentation results.

Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.

Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.

This skill analyzes your fitness and exercise data to identify workout trends, track progress over time, and correlate exercise with health metrics like blood pressure and blood sugar. It provides personalized training recommendations based on WHO/ACSM guidelines while respecting medical safety boundaries.

Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.