regression-modeling
Modeling relationships between variables using regression. Covers simple linear regression, multiple regression, polynomial regression, logistic regression, model fitting (least squares, maximum likelihood), residual analysis, model diagnostics, R-squared, adjusted R-squared, multicollinearity, variable selection, and the Box-Jenkins dictum that all models are wrong but some are useful. Use when predicting outcomes, quantifying relationships, building predictive models, or diagnosing model fit.
npx skills add majiayu000/claude-skill-registry --skill regression-modeling-tibsfox-gsd-skill-creator --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.
# Regression Modeling Regression models quantify the relationship between a response variable and one or more explanatory variables. The goal may be prediction ("what will Y be when X = 10?"), explanation ("how does Y change when X increases by one unit?"), or both. This skill covers the core regression toolkit from simple linear regression through logistic regression, with emphasis on the diagnostics that separate a useful model from a misleading one. **Agent affinity:** box (model building, diagnostics, "all models are wrong"), pearson (correlation, regression theory), efron (computational model fitting) **Concept IDs:** stat-descriptive-statistics, stat-hypothesis-testing ## Simple Linear Regression ### The model Y = beta_0 + beta_1 * X + epsilon, where epsilon ~ N(0, sigma^2). - **beta_0:** Y-intercept. The predicted value of Y when X = 0. - **beta_1:** Slope. The change in predicted Y for a one-unit increase in X. - **epsilon:** Error term. Captures everything the model does not explain. ### Least squares estimation The least squares estimates minimize the sum of squared residuals: b_1 = sum((x_i - x-bar)(y_i - y-bar)) / sum((x_i - x-bar)^2) b_0 = y-bar - b_1 * x-bar The fitte
- Simple Linear Regression
- The model
- Least squares estimation
- Interpretation
- R-Squared and Model Fit
- R-squared (coefficient of determination)
- Adjusted R-squared
- Cautions about R-squared
- Multiple Regression
- Interpretation with multiple predictors
- Multicollinearity
- Residual Analysis and Diagnostics
- Assumptions to check
- Residual plots
What does the regression-modeling skill do?
Modeling relationships between variables using regression. Covers simple linear regression, multiple regression, polynomial regression, logistic regression, model fitting (least squares, maximum likelihood), residual analysis, model diagnostics, R-squared, adjusted R-squared, multicollinearity, variable selection, and the Box-Jenkins dictum that all models are wrong but some are useful. Use when predicting outcomes, quantifying relationships, building predictive models, or diagnosing model fit.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill regression-modeling-tibsfox-gsd-skill-creator --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.