The recipe here for deming regression is almost identical to regular linear regression. The key difference is how we measure the loss between the predictions and the data points. Instead of a vertical loss, we have a perpendicular loss (or total loss) for the y and x values.
This type of regression is used when we assume that the errors in the x and y values are similar. We can also scale the x and y axes in the distance calculation by the difference in the errors, according to our assumptions.
