Principal Component Analysis of Determining Factors: Unveiling Key Influences

Given that the relationships among the four factors exhibit linear correlation, Principal Component Analysis (PCA) was employed to determine the most influential factor. PCA is a statistical technique that simplifies complex datasets by transforming a set of possibly correlated variables into a smaller set of linearly uncorrelated variables known as principal components. By analyzing the variance explained by each principal component, we can identify the factor that contributes most significantly to the overall variability in the data, thus revealing the determining factor among the four.