MATLAB Implementation of Least Squares Fitting

Before implementing least squares fitting in MATLAB, it is essential to determine the form of the fitting function. This step typically involves data analysis and preprocessing to select an appropriate functional model. Once the function form is established, MATLAB code can be written to perform the fitting using built-in functions like polyfit for polynomial fitting or lsqcurvefit for nonlinear optimization. During the fitting process, attention must be paid to data accuracy, reliability, and the precision/interpretability of fitting results. The implementation typically involves formulating the objective function to minimize the sum of squared residuals, where MATLAB's matrix operations efficiently handle normal equations for linear least squares problems. Furthermore, additional analysis and optimization of fitting results can be conducted using statistical tools like residual analysis and confidence intervals to better understand data characteristics and trends through MATLAB's visualization and computational capabilities.