Multivariate normality is a crucial assumption in many multivariate statistical methods, influencing the validity of medical data analyses. This study aims to develop and evaluate the multivariate normality of a dataset comprising biochemical parameters, specifically Total Cholesterol (TC), Urea, Creatinine (Creat), and Uric Acid (Uric). Using R and the MVN package, we developed a syntax to test for multivariate normality, applying Mardia’s skewness and kurtosis tests. The results indicated that the dataset meets the criteria for multivariate normality, with significant p-values confirming the assumption. Ensuring multivariate normality is essential for the validity of multivariate analyses in medical research. Our findings demonstrate that the biochemical parameters analyzed conform to the assumption, supporting their suitability for advanced statistical analyses. This study highlights the importance of verifying multivariate normality and provides a practical guide for researchers using R

Biochemical Parameters, Mardia’s Test, Medical Data, Multivariate Normality, R Programming, Statistical Analysis

The application of the multivariate normality test using the MVN package in R has demonstrated a rigorous approach to determining the multivariate normality of a dataset. By implementing this test on the given medical data, specifically focusing on variables like Total Cholesterol (TC), Urea, Creatinine (Creat), and Uric Acid (Uric), the syntax effectively assesses the assumption of multivariate normality, which is crucial for many statistical analyses. The test results, obtained through Mardia's multivariate normality test and visualized using a Q-Q plot, provide a comprehensive understanding of the data distribution. Mardia's test evaluates both skewness and kurtosis to determine normality, ensuring a thorough analysis. The successful execution of this test indicates whether the data conforms to a multivariate normal distribution, which is a key prerequisite for numerous multivariate statistical methods, such as MANOVA, discriminant analysis, and multivariate regression. In the context of medical and dental sciences, the ability to determine multivariate normality is particularly valuable. For instance, in medical research, where multiple biomarkers or clinical measurements are analyzed simultaneously, ensuring multivariate normality allows for more accurate modelling and hypothesis testing. This can lead to better diagnostic tools, treatment plans, and an understanding of complex relationships between various health indicators. Similarly, in dental research, where multiple oral health parameters are assessed, validating the multivariate normality assumption enhances the reliability of multivariate analyses, contributing to more effective interventions and preventive strategies. Overall, the successful application of this syntax not only demonstrates the practical utility of the MVN package in R for testing multivariate normality but also underscores its significance in the medical and dental sciences. By ensuring that the data meets the necessary assumptions for advanced statistical analyses, researchers can draw more robust and valid conclusions, ultimately improving patient outcomes and advancing the field of health sciences

Wan Muhamad Amir W Ahmad, Mohamad Nasarudin Adnan, Farah Muna Mohamad Ghazali, Nor Azlida Aleng, Mohamad Shafiq Mohd Ibrahim and Nurfadhlina Abdul Halim (2025). Evaluating Multivariate Normality in Medical Datasets: A Case Study with R. International Journal on Emerging Technologies, 16(1): 115–120