🧭 Regression — Quick References

This section contains concise, method-specific guides for both linear and logistic regression models.
Each quick reference distills key concepts, parameter settings, assumptions, and diagnostic tips to help practitioners apply the right regression approach for their problem while avoiding common pitfalls.
Whether you are modeling continuous outcomes with OLS, exploring regularized regression for high-dimensional data, or classifying outcomes with logistic regression, these guides offer fast, actionable insight.

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📂 Available Quick References

Linear Regression

• 🧭 ElasticNet Regression QuickRef — Combines L1 and L2 regularization for balanced feature selection and stability.
• 🧭 Lasso Regression QuickRef — L1 regularization for sparse models with automatic feature selection.
• 🧭 OLS + Robust Regression QuickRef — Ordinary least squares with robust alternatives for heteroskedasticity and outlier resistance.
• 🧭 Ridge Regression QuickRef — L2 regularization for multicollinearity handling and coefficient shrinkage.

Logistic Regression

• 🧭 Logistic Regression QuickRef — Standard binary classification with interpretable coefficients.
• 🧭 Multiclass Logistic Regression QuickRef — Extensions for handling more than two outcome categories.
• 🧭 Ordinal Logistic Regression QuickRef — Models ordered categorical outcomes with proportional odds.