The article discusses a method for improving linear regression analysis when dealing with uncertain data. It introduces a new way to calculate the total least squares, which considers both x and y variables as uncertain. By using this method, researchers can better handle cases where the precision of the data is not equal. The study provides formulas for this nonlinear computation and demonstrates its effectiveness through an example analysis. The results suggest that this approach can be valuable for processing engineering data more accurately.