Comprehensive regression model diagnostics and assumption checking. Use when validating regression models, checking assumptions (linearity, homoskedasticity, normality, independence), detecting outliers/influence, testing multicollinearity, or when user mentions residuals, heteroskedasticity, VIF, Cook's distance, or diagnostic plots.
El comando exacto puede variar según el repositorio. Consulta el README en GitHub.
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<skill_content>
<overview>
Regression diagnostics are NOT optional add-ons—they are fundamental to valid inference. A regression model is a hypothesis about the data-generating process, and diagnostics test whether that hypothesis holds. Without diagnostics, you're flying blind: your p-values may be wrong, your confidence intervals misleading, and your conclusions invalid. Every regression analysis MUST include diagnostics.
The consequences of ignoring diagnostics are severe: heteroskedasticit
[Description truncada. Veja o README completo no GitHub.]
