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Appendix A. Least squares polynomials and data fitting

Appendix A. Least squares polynomials and data fitting

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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

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