Multicollinearity, when predictor variables are highly correlated, was studied to see its impact on linear regression estimates. The researchers used Monte-carlo simulation to create data with high collinearity and tested different regression methods. They found that ordinary least squares had consistent bias across sample sizes, while lasso estimators varied. Ridge regression performed best with the least error at small sample sizes, while ordinary least squares was most accurate with larger sample sizes.