DocumentCode
1526168
Title
Fitting nature´s basic functions. I. Polynomials and linear least squares
Author
Rust, Bert W.
Author_Institution
Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA
Volume
3
Issue
5
fYear
2001
Firstpage
84
Lastpage
89
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;
fLanguage
English
Journal_Title
Computing in Science & Engineering
Publisher
ieee
ISSN
1521-9615
Type
jour
DOI
10.1109/MCISE.2001.947111
Filename
947111
Link To Document