Linear Least Squares Fitting Function

The lzxec function in MATLAB provides an implementation for linear least squares fitting. This function performs linear least squares fitting on discrete experimental data points to obtain the optimal fitting line, which can be used for future predictions or data analysis. Linear least squares fitting is a widely used data fitting method that employs the least squares algorithm to find the best-fit straight line by minimizing the sum of squared distances between all data points and the fitted line. The implementation typically uses matrix operations to solve the normal equations: (X'X)β = X'y, where X is the design matrix containing the predictor variables, y is the response vector, and β represents the coefficients to be estimated. The function may include features such as: - Input validation for data consistency - Calculation of regression coefficients (slope and intercept) - Computation of goodness-of-fit metrics (R-squared, RMSE) - Optional visualization of the fitted line against original data points Using the lzxec function simplifies the linear fitting process, making it more efficient, accurate, and accessible for various engineering and scientific applications. The function handles edge cases like singular matrices and provides meaningful error messages for invalid inputs.