regression-diagnostics

Name: regression-diagnostics
Rating: 5 (7 reviews)
Author: sshtomar

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.

7stars

Updated 3 months ago

View on GitHub ↗License: MIT

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/plugin marketplace add sshtomar/claude-code-skills-social-science

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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.