Regression Analysis

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.