Abstract
The aim of a least-squares approach is to estimate the parameters that define a known, or hypothetical, physical law. The estimated parameters are those that make the law fit a given set of data points in the least-squares sense. Probably the reader has carried through a least-squares curve fit more than once during his career. But, there is actually a great deal more to this problem than accomplishing the fit.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
L. Breiman: Statistics: With a View Toward Applications (Houghton Mifflin, Boston 1973) Chap. 10
M.G. Kendall, A. Stuart: The Advanced Theory of Statistics, Vol. 1 (Charles Griffin, London 1969)
Additional Reading
Bjerhammar, A.: Theory of Errors and Generalized Matrix Inverses (Elsevier Scientific, Amsterdam 1973)
Mendenhall, W., R.L. Scheaffer: Mathematical Statistics with Applications (Duxbury, North Scituate, MA 1973)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Frieden, B.R. (2001). Least-Squares Curve Fitting — Regression Analysis. In: Probability, Statistical Optics, and Data Testing. Springer Series in Information Sciences, vol 10. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56699-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-56699-8_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41708-8
Online ISBN: 978-3-642-56699-8
eBook Packages: Springer Book Archive