Gauss- Newton Nonlinear Regression

By Faouizi Amar

• Displays curve-fitting non-linear models using the Gauss-Newton regression method 
• Important for many industries 
• Shows how a FOR loop iterates through the algorithm until convergence of the model parameters is reached using Mathcad's in-line programming capabilities

     Curve fitting is often used to analyze and interpret experimental data, which is usually a large collection of points. The goal is to find a model function that fits the trend and level of the data. PTC Mathcad helps you to do this. 

     When fitting data to a particular model, it is common to transform the model to a linear one and then use some type of least squares regression. In many cases, it is not possible to transform the non-linear model into a linear one. Sometimes the transformation is possible but results in loss of sensitivity. Under such circumstances, direct non-linear regression should be used. There are many algorithms that have been developed specifically for this purpose. One such model is known as the Gauss-Newton algorithm.

     This Mathcad worksheet is used to implement the Gauss-Newton algorithm and shows how easy it is to ajdust and calculate using PTC Mathcad software.

     Download and explore this worksheet yourself! You can download a free lifetime copy of PTC Mathcad Express and get 30 days of full functionality.