in my statistics course, it was mentioned that for the Ordinary least squares method (OLS) taking variables $X_i$ and $Y_i$ as the response variable, the random variable is solely $Y_i$.
However for the Orthogonal regression method (ORM) both the $X_i$ and $Y_i$ are considered to be random variables.
From my understanding in the OLS we optimize the regression based on individually picked values, so for each $x_i$. However, in the ORM the optimization is not made for some fixed $x_i$ value. Is this the correct reason, or is there something else?