👉 Fitted math is a statistical method used to estimate the parameters of a mathematical model that best describe observed data. It involves finding the values of the model's parameters (such as coefficients in a regression equation) that minimize the discrepancy between the model's predictions and the actual data points. This is typically achieved using techniques like least squares optimization, where the goal is to minimize the sum of the squared differences (residuals) between observed and predicted values. The fitted model is then used to make predictions, infer relationships, or understand underlying patterns in the data. The quality of the fitted model is often evaluated using metrics like R-squared, which indicates how well the model explains the variance in the data, or through cross-validation to ensure robustness and generalizability.
