MATLAB Code Implementation for Linear Curve Fitting

In my custom-developed MATLAB program, providing a set of x and y coordinates as input yields a linear regression line defined by parameters (k, b). This implementation combines mathematical algorithms with computational programming, employing matrix operations and linear algebra concepts to solve the curve fitting problem. The core algorithm uses the least squares method through MATLAB's built-in matrix division operator (backslash) or pinv() function for robust coefficient calculation.

The program demonstrates strong versatility with applications across multiple domains, including map route plotting, astronomical data computation, and trend analysis. Through systematic data processing and computational analysis, the program accurately outputs regression results, enabling users to gain deeper insights into datasets and problem domains. The implementation includes error handling for singular matrix cases and provides residual analysis for fit quality assessment.

Additionally, the program features an intuitive user interface with interactive elements, allowing users to easily input coordinate data and visualize results. Designed with user experience principles, the interface incorporates MATLAB's GUI components like input fields and plot displays, making it accessible even for users without programming background. Data validation checks ensure input integrity before processing.

In summary, this program serves as a practical and efficient tool for handling coordinate-based linear regression problems. Its sophisticated implementation highlights the significance of mathematical computations and computer science applications in solving real-world problems, demonstrating how numerical methods and programming integration can create effective analytical solutions. The code structure follows MATLAB best practices with modular functions for data input, matrix computation, and result visualization.