What does scaling the data in regression analysis help to achieve?

Scaling the data in regression analysis enhances the model's interpretability by standardizing the range of the data features, allowing for a more straightforward comparison of the coefficients. When the features are not on the same scale, coefficients can be misleading, as a larger value might imply more influence over the target variable simply due to its scale rather than its actual importance.

Standardization or normalization of data allows for clearer interpretation, as it places all variables on a comparable footing. This means that stakeholders can better understand the impact of each variable on the outcome, as changes can be assessed in terms of standardized units. Additionally, it can also facilitate convergence during the optimization process in training regression models by preventing variables with larger scales from dominating the learning algorithms.

The other options, while related to regression analysis, do not specifically describe the primary intent of scaling data. Reducing variables and addressing multicollinearity involve different techniques and concepts, while data visualization focuses more on representation rather than interpretation of model coefficients.