ABSTRACT
This chapter focuses on the analytical methods to arrive at a model for a given data set using a prescribed criterion. It describes three curve fitting criterion: least squares, Chebyshev’s criterion, and minimizing the sum of the absolute error. The method of least-squares curve fitting, also known as ordinary least squares and linear regression, is simply the solution to a model that minimizes the sum of the squares of the deviations between the observations and predictions. Correlation is a measure of the linear relationship between variables. In Maple, the Fit command from the Statistics package fits a model curve to a set of data points using least-squares methods. The chapter presents the Maple commands that solve the least-squares optimization problem and provides an analysis of the adequacy of the resulting model. In Maple, the Fit command from the Statistics package fits a model curve to a set of data points using least-squares methods.
