The total least squares estimation is a method used in statistics to find the best fit line for data points. It is similar to the least squares estimation but has some differences. The total least squares estimation is unbiased, but it is more sensitive to errors in the data compared to the least squares estimation. This means that small errors in the data can have a bigger impact on the total least squares estimation. The total least squares estimation also has a higher condition number than the least squares estimation. This research shows that the total least squares and least squares estimations have different solutions, residuals, and unit weight variance estimations.