Title :
Fitting nature´s basic functions. I. Polynomials and linear least squares
Author_Institution :
Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA
Abstract :
The problem of fitting a mathematical model which depends on an n-vector of unknown parameters, to a measured data set is ubiquitous in science and engineering. This paper is the first installment of a series that will demonstrate modern techniques for fitting combinations of basic mathematical functions to measured real-world data. Fitting a straight line, linear least squares and the best linear unbiased estimate are discussed.
Keywords :
least squares approximations; polynomials; data set; linear least squares; linear unbiased estimate; mathematical functions; mathematical model fitting; n-vector; polynomials; straight line fitting; Data engineering; Equations; Gaussian processes; Least squares approximation; Least squares methods; Measurement errors; Polynomials; Predictive models; Prototypes; Temperature;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCISE.2001.947111
