Society of Actuaries PA Practice Exam Study Guide

If a factor level has a significant number of observations compared to others, it can skew the results of your analysis and may lead to biased conclusions. Evaluating whether to combine levels or exclude them allows for a more balanced dataset, which can improve the reliability of your model.

Combining levels can help to simplify the analysis and ensure that the model doesn't focus excessively on the dominant level, which could otherwise overshadow the contributions of less frequent levels. Conversely, excluding levels may be appropriate if they lack significance or lead to noise in the results.

Maintaining a thoughtful approach to how levels are treated, rather than simply keeping all levels or removing the variable altogether, ensures that the analysis remains robust and that the insights derived from the data reflect more accurately the underlying distributions and relationships. This careful consideration can enhance the overall effectiveness of statistical modeling and decision-making processes based on that model.