NDLI: An algorithm to the method of curve fitting by the process of least squares

Content Provider	ACM Digital Library
Author	Marcus, L.
Abstract	The importance of determining an analytical function to fit a set of experimental data has long since been recognized. Efforts have been made to find reliable methods of relating an analytical function to a set of coordinates, and at the same time, to avoid the dangerous loss of significant figures in the computation. It is well known that the classical theory of least squares is one of the best methods for fitting an analytical function to a set of experimental data. However, using this theory, the exact equation to be fitted must be chosen beforehand. Hence, the final form of an equation using the minimum number of terms required can only be obtained by a method requiring trial and error. The method to be outlined in this paper eliminates the trial and error computation in determining approximately the minimum number of terms in the final equation. It also has the advantage of avoiding the solution of an almost singular set of simultaneous equations.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
DOI	10.1145/800258.808934
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1956-01-01
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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An algorithm to the method of curve fitting by the process of least squares