Data Processing and Error Analysis Using MATLAB

Data processing and error analysis are implemented using MATLAB software. Although MATLAB's interface is less intuitive compared to VB, it provides extensive built-in functions that make implementation more convenient. The implemented features in the program include: (1) Calculation of arithmetic mean using mean() function; (2) Computation of residual errors (absolute errors) through array operations; (3) Standard deviation calculation using std() function; (4) Gross error detection using statistical threshold methods (e.g., 3σ criterion) with automatic elimination and recalculation; (5) Identification of linear or periodic errors through trend analysis and Fourier transform techniques. Additionally, MATLAB enables various other data processing and error analysis operations such as data fitting (using polyfit() or fit() functions), regression analysis (via regress() or fitlm()), and advanced statistical computations. The implementation typically involves vectorized operations for efficiency and utilizes MATLAB's robust numerical algorithms. Using MATLAB significantly enhances the efficiency and accuracy of data processing and error analysis tasks through its comprehensive mathematical libraries and visualization capabilities.