Ignoring collinearity among predictors can lead to issues in statistical analysis. To address this, principal components were used to combine related variables into new independent ones. By applying this method to a sample of 600 participants, the researchers successfully eliminated multicollinearity. The results showed that using principal components as predictors in a regression model improved estimation and prediction accuracy. This study demonstrates that principal component analysis is an effective solution for dealing with collinearity among variables.