Regression Analysis

Welcome to the Regression Analysis module. Here you will learn the fundamental techniques for modeling relationships between variables, validating those models, and applying regularization to prevent overfitting.

Module Contents

1. Simple Linear Regression

Understand the “Genesis” of OLS (Gauss-Markov Theorem), the “Hardware Reality” of floating-point precision, and implement Linear Regression in Go, Java, and Python.

2. Residual Analysis & Diagnostics

Learn to validate your model using the LIHN assumptions. Understand why residuals must be “white noise” (Information Theory) and avoid the “Autocorrelation Trap.”

3. Regularization: Ridge & Lasso

Tackle the Bias-Variance Tradeoff. Understand how Lasso creates sparse matrices to save memory (Hardware Reality) and visualize L1 vs L2 penalties.

Review & Cheat Sheet

Test your knowledge with interactive flashcards and access a quick-reference cheat sheet for formulas and Go/Java/Python code.