MATLAB Source Code for Normal Probability Paper Method

The MATLAB source code for implementing the normal probability paper method provides a robust tool for conducting normality tests. This statistical method is widely employed to verify whether a given dataset follows a normal distribution - a fundamental assumption underlying numerous statistical analyses and hypothesis tests. The program implementation typically involves several key algorithmic steps: first calculating the empirical cumulative distribution function of the dataset, then transforming the data onto normal probability paper coordinates using quantile-quantile (Q-Q) plot principles. The core functionality likely includes data sorting, probability plotting positions computation (using methods like Hazen's or Weibull's formula), and linear regression analysis to assess deviation from normality. Key MATLAB functions utilized may include: - sort() for data ordering - norminv() for inverse normal distribution calculations - plot() for visualization with specialized axis scaling - polyfit() for linear trend analysis - Statistical toolbox functions for enhanced normality assessment Researchers and data analysts can leverage this MATLAB program to efficiently perform normality testing with visual validation through probability plots. The code architecture allows for customization of plotting parameters, confidence interval calculations, and hypothesis testing thresholds. The modular design enables easy modification to accommodate specific research requirements, such as handling different sample sizes or incorporating additional normality tests like Shapiro-Wilk or Kolmogorov-Smirnov. This implementation serves as an essential resource for statistical validation in research projects, quality control processes, and data analytics workflows. The program enhances analytical accuracy by providing both visual and quantitative assessments of normality, while the MATLAB environment ensures computational efficiency and reproducibility of results.