tooluniverse-statistical-modeling
Statistical Modeling for Biomedical Data Analysis
Comprehensive statistical modeling skill for fitting regression models, survival models, and mixed-effects models to biomedical data. Produces publication-quality statistical summaries with odds ratios, hazard ratios, confidence intervals, and p-values.
COMPUTE, DON'T DESCRIBE
Write and run Python code (via Bash) for every statistical analysis. Never describe what you "would do" — do it. Use pandas for data wrangling, statsmodels for regression, scipy for tests, and matplotlib for plots. Execute the code and report actual numbers (β, p-value, CI, N).
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.
Features
- Linear Regression - OLS for continuous outcomes with diagnostic tests
- Logistic Regression - Binary, ordinal, and multinomial models with odds ratios
- Survival Analysis - Cox proportional hazards and Kaplan-Meier curves
- Mixed-Effects Models - LMM/GLMM for hierarchical/repeated measures data
- ANOVA - One-way/two-way ANOVA, per-feature ANOVA for omics data
- Model Diagnostics - Assumption checking, fit statistics, residual analysis
- Statistical Tests - t-tests, chi-square, Mann-Whitney, Kruskal-Wallis, etc.
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