Data Distribution Fitting and Testing in MATLAB

This MATLAB source code for data distribution fitting and testing has been thoroughly debugged and includes implementations for Chi-square Goodness-of-Fit Test, Jarque-Bera Test (jbtest), Kolmogorov-Smirnov Test, and frequency histogram plotting. Additionally, it provides data preprocessing and data visualization capabilities. The Chi-square Goodness-of-Fit Test implementation enables hypothesis testing to determine whether data follows a specific known distribution, using chi2gof function with appropriate binning strategies and parameter estimation. The jbtest function implementation specifically tests for normal distribution compliance by analyzing sample skewness and kurtosis parameters. The Kolmogorov-Smirnov Test (kstest) implementation provides a non-parametric method to test data against any continuous distribution, comparing empirical distribution functions with theoretical distributions. Beyond these statistical tests, the code includes frequency histogram plotting functionality using MATLAB's histogram function with customizable bin widths and normalization options, allowing better understanding of data distribution characteristics through visual analysis. The package also incorporates comprehensive data preprocessing features. Through preprocessing modules, users can perform data cleaning operations including missing value handling using interpolation methods, outlier detection and treatment using statistical thresholds (e.g., 3-sigma rule), and data normalization techniques to ensure data quality and accuracy for subsequent analysis. Finally, the code provides extensive data visualization capabilities through MATLAB's plotting functions. Users can generate scatter plots using scatter function for correlation analysis, line charts using plot function for trend visualization, and bar charts using bar function for categorical data representation, enabling more intuitive data presentation and exploratory analysis. These implemented features are designed to meet various data analysis requirements and assist users in better understanding and interpreting their dataset characteristics through both statistical testing and visual exploration methods.