Six Sigma Black Belt Certified Practice Exam

The correct approach to reduce higher-order terms in a regression model is through transformations. Transformations involve modifying the original variables in such a way that their relationship with the dependent variable becomes more linear, often leading to simpler models with fewer higher-order terms. This can include techniques like logarithmic, square root, or reciprocal transformations that help stabilize variance and normalize the distribution of residuals.

Higher-order terms, such as squared or cubed variables, can complicate models by introducing non-linearity. By applying transformations, you can often capture the essence of the relationship without needing to include these higher-order terms, thus simplifying the model and improving interpretability.

While large samples can increase the robustness of regression results and dummy variables can help represent categorical data, neither directly addresses the challenge of higher-order terms. Similarly, blocking is primarily used in experimental designs to account for variation, rather than specifically reducing model complexity related to polynomial terms. Therefore, transformations stand out as the vital technique to achieve a more parsimonious regression model.