The article tests different methods to improve accuracy in regression analysis when variables are correlated. The researchers focused on Poisson Regression models and used a metric called Average Mean Square Error (AMSE) to compare the performance of 50 estimators. They found that having fewer variables and a higher intercept value led to better estimator performance. Some estimators consistently outperformed others across different correlation levels, sample sizes, intercept values, and numbers of explanatory variables.