Nonlinear Regression Modeling of Data Employing the Gauss-Newton Method
Date of Graduation
Master of Science in Mathematics
error sum of squares, Gauss-Newton method, linear regression, nonlinear regression, residual plot analysis
Linear regression models are useful for estimating statistical relationship between related variables of a data set. In many practical situations a linear regression model is not an appropriate fit for the data. In this thesis, we analyze data by nonlinear regression methods to determine an appropriate relation between the variables involved. The estimation of the parameters involved in the nonlinear regression model is nearly impossible by any closed-form solution approached. The modeling is done by iteration procedures involving the Gauss-Newton method. A comparison of the results is also provided.
© Michael William Edwards
Edwards, Michael William, "Nonlinear Regression Modeling of Data Employing the Gauss-Newton Method" (2006). MSU Graduate Theses. 1625.