🧭 Linear Regression — Quick References

This section contains targeted reference guides for various linear regression modeling techniques.

These quick references distill the key concepts, formulas, and implementation tips needed to apply and interpret each model effectively.

Whether you're fitting a simple OLS model, applying regularization to combat multicollinearity, or leveraging robust methods to handle outliers, these guides serve as a fast, reliable resource for your modeling workflow.

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📂 Available Quick References

• 🧭 ElasticNet Regression QuickRef — Combines L1 and L2 regularization for balanced feature selection and shrinkage.
• 🧭 Lasso Regression QuickRef — L1-regularized regression for feature selection and sparse models.
• 🧭 OLS + Robust Regression QuickRef** — Ordinary Least Squares with robust alternatives for outlier resistance.
• 🧭 Ridge Regression QuickRef — L2-regularized regression to reduce variance and stabilize coefficients.