  An important problem of optimization analysis surges when parameters such as
$ \{\theta_j\}_{j=1,\, \dots \,,k }$, determining a function $
y=f(x\given\{\theta_j\}) $, must be estimated from a set of observables $ \{
x_i,y_i\}_{i=1,\, \dots \,,m} $. Where $ \{x_i\} $ are independent variables
assumed to be uncertainty-free. It is known that analytical solutions are
possible if $ y=f(x\given\theta_j) $ is a linear combination of $
\{\theta_{j=1,\, \dots \,,k} \}.$ Here it is proposed that determining the
uncertainty of parameters that are not \textit{linearly independent} may be
achieved from derivatives $ \tfrac{\partial f(x \given \{\theta_j\})}{\partial
\theta_j} $ at an optimum, if the parameters are \textit{stochastically
independent}.
