Residual analysis is a crucial Six Sigma tool used to assess the accuracy and validity of regression models. By examining the residuals—differences between observed and predicted values—you can identify patterns, outliers, and potential model inaccuracies. This analysis helps ensure that assumptions of linearity, independence, and homoscedasticity are met. It aids in detecting model inadequacies, improving predictive accuracy, and refining process models. Residual analysis supports data-driven decision-making, process optimization, and continuous improvement by providing insights into model performance and guiding necessary adjustments for better quality and efficiency in Six Sigma projects.
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