Linear regression finds the best-fit line through a dataset: y = mx + b.
R² close to 1 = strong model fit. R² near 0 = poor fit.
The slope (m) indicates how much Y changes per unit increase in X.
Linear regression finds the line of best fit: y = mx + b, minimizing the sum of squared residuals.