Appendix A. Least squares polynomials and data fitting

Appendix A. Least squares polynomials and data fitting

For access to this article, please select a purchase option:

Buy chapter PDF
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Sensors, Actuators, and Their Interfaces: A multidisciplinary introduction — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Least square polynomials or polynomial regression is a method of fitting a polynomial to a set of data. Passing a polynomial through a set of data means selection of the coefficients so as to minimize, in a global sense, the distance between the value of the function y(x) and the values at the points. This is done through the least squares method by first writing the "distance" function.

Chapter Contents:

  • C.1 Representation of numbers on microprocessors
  • B.1 Type J thermocouples (iron/constantan)
  • A.1 Linear least square data fitting
  • C.1.1 Binary numbers: unsigned integers
  • B.2 Type K thermocouples (chromel/alumel)
  • A.2 Parabolic least squares fit
  • C.1.2 Signed integers
  • B.3 Type T thermocouples (copper/constantan)
  • B.4 Type E thermocouples (chromel/constantan)
  • B.5 Type N thermocouples (nickel/chromium–silicon)
  • B.6 Type B thermocouples (platinum [30%]/rhodium–platinum)
  • B.7 Type R thermocouples (platinum [13%]/rhodium–platinum)
  • B.8 Type S thermocouples (platinum [10%]/rhodium–platinum)

Inspec keywords: polynomials; least squares approximations; regression analysis

Other keywords: polynomial regression; distance function; least squares polynomials; data fitting

Subjects: Interpolation and function approximation (numerical analysis); Other topics in statistics; Numerical approximation and analysis; Probability theory, stochastic processes, and statistics; Numerical analysis; Other topics in statistics; Interpolation and function approximation (numerical analysis); Statistics

Related content

This is a required field
Please enter a valid email address