Nonlinear-fitting-using-orthogonal-regression
When performing non-linear curve fitting to experimental data, one may encounter the need to account for errors in both independent variables and dependent variables. In Origin, you can utilize the Orthogonal Distance Regression (ODR) to fit your data with implicit or explicit functions. This tutorial will demonstrate how to perform non-linear curve fitting on data with both X errors and Y errors using ODR with a built in function.
Minimum Origin Version Required: Origin 9.1
This tutorial will show you how to use Orthogonal Distance Regression to fit nonlinear data with both X and Y errors.
You can refer to this page for the details of the algorithm of ODR as well as Levenberg Marquardt (L-M) algorithm. Another example of using Orthogonal Distance Regression for Implicit Functions can be found here.