The study of catalytic reactions often involves handling large datasets, where certain data points may be more significant than others due to their impact on reaction outcomes. By applying WMA, researchers can prioritize these important data points, leading to more accurate and insightful analysis. This method is particularly useful when dealing with time-series data or datasets with inherent variability.
