Catalysis involves numerous variables and complex interactions, making data analysis challenging. PCA simplifies this by transforming the original variables into a new set of uncorrelated variables called principal components. This transformation makes it easier to visualize and interpret the data, facilitating the discovery of key factors that influence catalytic performance.
