The main advantage of using a stepwise regression approach is:

Using a stepwise regression approach primarily simplifies the selection of variables by automating the process of determining which predictors (independent variables) should be included in the model and which should be excluded. This method iteratively adds or removes variables based on their statistical significance, allowing researchers to focus on the most impactful predictors without needing to manually try every combination.

This automatic variable selection process is particularly helpful in situations where there are many potential predictors, as it saves time and reduces complexity. It enables analysts to build models that are not only significant but also parsimonious, meaning they retain only the most meaningful predictors relevant to the dependent variable.

In contrast to the other options, while stepwise regression can provide a streamlined approach, it does not guarantee the absolute best model, nor does it negate the requirement for fundamental statistical understanding. Additionally, data is still required for any regression analysis, as the method relies on existing data to derive relationships between variables.