statistical-modeling
Regression analysis, ANOVA, generalized linear models, Bayesian methods, and model selection. Covers the full modeling workflow from problem formulation through diagnostics -- linear regression, logistic regression, Poisson regression, mixed-effects models, prior specification, posterior inference, AIC/BIC comparison, cross-validation for model selection, and assumption checking. Use when fitting models, testing hypotheses, or selecting among competing statistical explanations.
npx skills add majiayu000/claude-skill-registry --skill statistical-modeling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Statistical Modeling Statistical modeling is the practice of fitting mathematical structures to data in order to quantify relationships, test hypotheses, and make predictions. Unlike machine learning, which optimizes prediction, statistical modeling privileges interpretability and inference -- understanding *why* variables relate, not just *that* they do. Leo Breiman's "two cultures" paper (2001) crystallized this distinction. This skill covers the inferential tradition while acknowledging where the two cultures overlap. **Agent affinity:** tukey (EDA and diagnostics), fisher (experimental design and ANOVA), breiman (model comparison) **Concept IDs:** data-hypothesis-testing, data-confidence-intervals, data-correlation, data-normal-distribution ## The Modeling Workflow | Stage | Goal | Key operations | |---|---|---| | 1. Formulation | Define the question as a model | Specify response variable, predictors, functional form | | 2. Exploration | Understand data structure | Scatterplots, correlation matrices, distribution checks | | 3. Fitting | Estimate parameters | OLS, MLE, MCMC, IRLS depending on model class | | 4. Diagnostics | Check assumptions | Residual plots, Q-Q plots, lever
- The Modeling Workflow
- Linear Regression
- The Model
- Assumptions (LINE)
- Interpretation
- Multicollinearity
- Logistic Regression
- Assumptions
- Generalized Linear Models (GLMs)
- Common GLMs
- Poisson Regression
- Analysis of Variance (ANOVA)
- Purpose
- One-Way ANOVA
What does the statistical-modeling skill do?
Regression analysis, ANOVA, generalized linear models, Bayesian methods, and model selection. Covers the full modeling workflow from problem formulation through diagnostics -- linear regression, logistic regression, Poisson regression, mixed-effects models, prior specification, posterior inference, AIC/BIC comparison, cross-validation for model selection, and assumption checking. Use when fitting models, testing hypotheses, or selecting among competing statistical explanations.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill statistical-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.