Abstract
This chapter discusses the analysis of data using regression analysis. Data can be analyzed by both linear and nonlinear regression analysis. The main characteristic of linear models is that the measured quantity is linearly dependent upon the parameters in the model. Classifying a model as a linear model means that it can be fitted into experimental data by the method of linear regression. Linear regression is also called linear least squares. The chapter discusses the principles of least squares. The principle of least squares is used in both linear and nonlinear regression. Single equation nonlinear models are those in which the dependent variable y depends in a nonlinear fashion on at least one of the parameters in the model. The goal of linear and nonlinear regression is to find the absolute minimum in the error sum with respect to all the parameters. Minimization algorithm and modified simplex algorithm are used for nonlinear regression.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
