MATLAB Code Implementation for Surface Fitting

This document introduces a MATLAB surface fitting program, a highly valuable tool that employs the least squares method to achieve optimal approximations. The least squares algorithm is a widely-used mathematical approach for solving data fitting problems, implemented in MATLAB through functions like lsqcurvefit or polyfit for polynomial surfaces. This program finds applications across multiple domains including engineering, science, and economics. In engineering fields, it can fit 3D shapes of mechanical components for machining and manufacturing purposes, typically using grid data interpolation with meshgrid and surface functions like surf or mesh. In scientific research, it helps fit observational data to better understand natural phenomena, often employing curve fitting toolbox functions or custom optimization algorithms. In economics, it can model market trends to support better business decisions, potentially using regression techniques with fitlm or fitnlm functions. The MATLAB surface fitting program demonstrates practical utility through its ability to handle scattered data points using scatteredInterpolant, and can implement various fitting techniques including polynomial regression, spline interpolation, or radial basis functions. Overall, this MATLAB surface fitting program serves as an efficient tool for data processing and workflow enhancement, offering both linear and nonlinear fitting capabilities with customizable error metrics and visualization options.