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regression-diagnostics

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.

7estrelas
Atualizado há 7 meses

Ver no GitHub ↗ Licença: MIT

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Ler descrição completa

<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.]

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