Total Least Squares

Total Least Squares (TLS) is a statistical method used to find the best-fitting model for data with measurement errors in both the independent and dependent variables. Unlike ordinary least squares, which only considers errors in the dependent variable, TLS accounts for inaccuracies in all variables. This approach typically minimizes the orthogonal distances from the data points to the fitting model, providing a more accurate representation of the underlying relationship when errors are present. TLS is particularly useful in fields like engineering and data science, where precision in measurements is crucial for reliable results.