Orthogonal regression

Orthogonal regression, also known as total least squares, is a method used to find the best-fitting line or curve through a set of data points when there are errors or uncertainties in both the x (independent) and y (dependent) variables. Unlike standard regression that minimizes vertical distances, orthogonal regression minimizes the shortest distances (perpendiculars) from each data point to the fitted line. This approach provides a more balanced and accurate model when measurement errors affect both variables, helping to understand the true relationship between them.