# Understanding orthogonal regression

In orthogonal regression, we are trying to minimize the distance from each data point $$(x,y)$$ to the fitted model.

My question is, how come that there is a distinction between independent and dependent variables in orthogonal regression?

In my naive understanding, the fit we are trying to achieve does not distinguish between $$x,y$$ - we want to find the line that minimizes the sum of distance to each $$(x_i,y_i)$$.