Regression equation expresses relationship mathematically. Simple regression: Ŷ = a + bX estimates Y from single X. Multiple regression: Ŷ = a + b₁X₁ + b₂X₂ + ... uses multiple predictors. Key concepts: coefficients represent marginal effects, intercept is baseline prediction. Testing significance: t-test for individual coefficients, F-test for overall model. Common traps: multicollinearity (predictors correlated), over-fitting, extrapolation beyond data range. Exam tips: interpret coefficients in context, check coefficient signs. Time-saving: standardized coefficients (β) compare relative importance. Adjusted R²: accounts for number of predictors. Assumptions: linearity, independence, normality, constant variance. Applications: business forecasting, economic modeling. Understanding equation interpretation crucial for predictions. Practice with business datasets.