Curve Fitting Introduction covers fundamental concepts. Purpose: find mathematical function approximating dataset behavior. Fitting categories: linear (Y = a + bX), exponential, polynomial, power, logarithmic. Key concepts: minimize errors between actual and predicted values. Common traps: over-fitting to noise, selecting overly complex models. Exam tips: start with simplest model, visualize data first. Time-saving: recognize data pattern visually. Selection criteria: R-squared, residual analysis, practical interpretability. Least squares principle: standard fitting technique. Residuals: differences between observed and predicted values. Model assessment: plot residuals to check assumptions. Applications: growth modeling, sales forecasting, trend prediction. Parsimony principle: prefer simpler models with similar fit. Understanding fitting foundation for predictive modeling. Practice visual data interpretation.