Which technique can be used in regression analysis to reduce higher-order terms in a model?

• A. Linear regression
• B. Transformations
• C. Normalization
• D. Codification

Answer: (B)

Transformations are a valuable technique in regression analysis, particularly when it comes to simplifying models by reducing higher-order terms. In regression, models that include higher-order polynomial terms (like quadratic or cubic terms) can become overly complex, potentially leading to issues such as overfitting and multicollinearity.

By applying transformations—such as logarithmic, square root, or Box-Cox transformations—you can often linearize relationships, stabilize variance, or make the data conform more closely to normality. This can help streamline the model and focus on the most significant predictors, while still capturing the essential dynamics of the relationship being modeled.

Transformations enable a researcher to reduce the complexity of the model without losing the critical information about the relationship between the variables. This enhances interpretability and generalizes the findings better to new data.

While linear regression and normalization have their respective uses, they do not specifically address the issue of reducing higher-order terms in models. Codification, a technique used for categorical variables, also does not apply in this context. Thus, transformations stand out as the appropriate method for simplifying regression models.