Useful Links

Explains linear regression using an engaging analogy of predicting drag show tips. It introduces the "line of best fit" and how to use SSE (Sum of Squared Errors) to measure accuracy.

A deep dive into error metrics: SSE, MSE, and RMSE. It explains how minimizing these errors leads to the best fit and why RMSE is often the most interpretable metric.

A minimal, from-scratch implementation of Linear and Polynomial regression in Python. Great for understanding the code and math behind the models without heavy frameworks.

Covers the fundamentals of the linear equation (y = a + bx) and how to interpret slope and intercept. Includes practical examples and Python code for implementation.