The article explores ways to deal with multicollinearity in regression analysis. It focuses on using shrinkage estimators to reduce the impact of multicollinearity, especially in systems without negative feedback. The researchers found that the Principal Component estimator is not suitable for systems with negative feedback. By studying the distribution of a random variable, they determined that the choice of regularization parameter or number of principal components can help in reducing the Euclidean distance between estimates and true coefficients. Alternative methods like Inequality Constrained Least Squares and the Dual estimator were also examined to address multicollinearity effectively.