Linear Regression: Basics and Principle of Least Squares

This lecture covers the transition from ANOVA with discrete factors to linear regression with continuous factors, focusing on finding coefficients that minimize the error using the principle of least squares. The instructor explains the model, the goal of minimizing the error, and the statistically best choice of parameters. The lecture also includes an ANOVA table for linear regression and a practical example in R to build a linear model. Key concepts discussed include the model equation, error minimization, and statistical analysis.