Regression & Data

What is Data?

Averages & Central Tendency

Spread: Variance & Std Dev

Correlation

Linear Regression

Making Predictions & R²

Overfitting & Model Evaluation

Assumptions of Linear Regression

Feature Engineering for Regression Models

Advanced Model Evaluation and Selection Strategies

Diagnosing & Addressing Model Problems

Categorical Variables in Regression

Introduction to Logistic Regression

Beyond Linear: Polynomials & Regularization

Interpreting and Explaining Complex Regression Models

Handling Multicollinearity and High-Dimensionality

Robust Regression and Quantile Regression

Time Series Regression and Forecasting

Tree-Based Regression: Random Forests and Gradient Boosting

Causal Inference with Regression: From Correlation to Causation