What is a common reason for transforming input variables in regression analysis?

In regression analysis, transforming input variables is often essential for ensuring that the assumptions of the model are satisfied. One common rationale for performing these transformations is to help match model assumptions. Many regression techniques rely on certain assumptions, such as linearity, homoscedasticity (constant variance of errors), and normality of residuals. When input variables are transformed—whether by applying logarithmic, square root, or other forms of transformation—it can stabilize variance and improve the relationship between predictors and the response variable, leading to better model performance.

Transformations can also have the effect of making the relationship between variables more linear, which is especially important when the original data exhibit a nonlinear relationship. By matching the assumptions necessary for effective regression analysis, transformed variables can contribute to a more reliable and interpretable model.

While reducing bias in predictions is a potential benefit of some transformations, the primary reason they are applied is to align with the foundational assumptions of the regression model itself, ensuring that the analysis produces valid and actionable insights.