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Fitting Nature's Basic Functions I. Polynomials and Linear Least Squares



Bert W. Rust


This paper is the first in a series on fitting combinations of basic mathematicalmathematical functions to measured data from the real world. This installmentexplains linear least squares with a special emphasis on polynomial fits. It describesthe statistical assumptions in the general linear model and explains why the leastsquares calculation gives the best linear unbiased estimate for the unknown parameters. It uses the measured global average annual temperature record for the years 1856-1999 as an example data set and compares the extrapolations into the future of various polynomial fits to that data set.
Computing in Science & Engineering
No. 5


global temperatures linear estimation, linear least squares, linear regression


Rust, B. (2001), Fitting Nature's Basic Functions I. Polynomials and Linear Least Squares, Computing in Science & Engineering, [online], (Accessed April 22, 2024)
Created October 1, 2001, Updated February 17, 2017