Statistical Analysis in Multicorrelation Test Conditions: Confronting the Incompatibility of Classical Assumptions

Abstract

Explore the impact of multicorrelation on classical assumptions in statistical analysis and identify more sensitive identification methods and innovative treatment strategies. Through analysis of simulated data and comparison of different methods, we show how multicorrelation can affect the validity of statistical analysis results. The use of correlation matrix analysis methods, Variance Inflation Factor (VIF), and other approaches were evaluated to identify the level of multicorrelation in the dataset. Additionally, we propose treatment strategies that include variable deletion, data transformation, and use of alternative regression methods. The results show that more detailed treatment methods provide a more adaptive solution to the multicorrelation problem, increasing the validity of the statistical analysis.This research underscores the importance of careful identification of multicorrelations and use of appropriate treatment strategies to maintain the validity of classical assumptions in statistical analysis. These results can serve as a guide for practitioners and researchers in facing the challenges of multicorrelation in data analysis, as well as provide a foundation for further research in this area.