What is regression analysis used for within Six Sigma?

Regression analysis is a statistical method that is widely utilized in Six Sigma to identify relationships between variables and predict outcomes. This analysis involves examining the association between dependent and independent variables, allowing practitioners to understand how changes in one variable might affect another.

In the context of Six Sigma, regression analysis is particularly valuable because it helps teams make data-driven decisions by uncovering correlations in data that can guide process improvements. For instance, by analyzing factors affecting quality or performance, teams can predict how changes in a given variable—like process input levels—will impact the end result, such as defect rates or cycle times. This predictive capability enables organizations to target areas for improvement more effectively, enhancing overall quality and operational efficiency.

The other options focus on aspects of Six Sigma that do not directly pertain to the core function of regression analysis. Eliminating waste pertains more to methodologies like Lean Six Sigma. Forecasting sales and market trends relates to business strategy rather than process improvement, whereas setting performance benchmarks involves goal-setting and measuring success rather than the analytical relationships that regression analysis seeks to establish.