Society of Actuaries (SOA) PA Practice Exam 2026 – Comprehensive All-in-One Guide to Mastering Your Exam Success!

The method that starts with all candidate variables and removes them based on fit criteria is known as Backward Selection. In this approach, the full model is initially created with every available variable. The selection process then evaluates the contribution of each variable to the model's performance, typically using metrics such as p-values or significance levels. Variables that do not meet the predefined criteria for inclusion are sequentially removed from the model.

This method is particularly useful when a comprehensive understanding of all variables at the outset is desired, allowing the modeler to identify which variables might need to be excluded without starting from a minimal variable set. This contrasts with other selection methods like Forward Selection, which starts with no variables and adds them based on their contribution to improving the model, or Stepwise Selection, which combines elements of both forward and backward approaches. Random Selection does not rely on criteria for variable fit and does not systematically assess variables in terms of their contribution to the model.