Regression analysis applications extend to business decisions. Prediction: use equation to estimate Y for new X values (interpolation). Caution with extrapolation: predictions beyond data range unreliable. Confidence intervals for predictions: account for prediction uncertainty. Key concepts: standard error measures prediction variability, wider intervals for extreme X values. Common traps: over-relying on poor-fit models, ignoring assumption violations. Exam tips: verify model quality before predictions, state prediction uncertainties. Time-saving: use software for complex calculations, focus on interpretation. Applications: sales forecasting, cost estimation, demand planning. Sensitivity analysis: how predictions change with input variations. Diagnostic plots: residual plots reveal assumption violations. Understanding limitations crucial for responsible predictions. Practice with realistic business scenarios.