The study compared two methods of analyzing data: ordinary least squares regression and orthogonal regression. Orthogonal regression considers uncertainties in multiple variables, making it more stable than ordinary regression. The analysis showed that errors in orthogonal regression are lower than in ordinary least squares regression. Additionally, orthogonal regression is not affected by the orientation of the data. This was confirmed through examples of fitting lines in two-dimensional space.