Linear regression models relationship: Y = a + bX where a is intercept, b is slope. Least squares method: minimizes Σ(Y - Ŷ)². Formulas: b = Σ[(x-x̄)(y-ȳ)] / Σ(x-x̄)², a = ȳ - b x̄. Key concepts: regression line passes through (x̄, ȳ), slope indicates rate of change. Common traps: confusing slope and intercept, using wrong formula. Exam tips: verify calculations, check sign of slope. Time-saving: use shortcut b = r(sy/sx) where r is correlation. R² (coefficient of determination): proportion of variance explained (0 to 1). Residuals: differences between actual and predicted values. Applications: sales forecasting, cost estimation, trend analysis. Understanding linear regression foundation for advanced modeling. Practice with various datasets and interpretations.