Abstract
The fitting of a linear function (or, more generally, of a polynomial) to measured data that depend on a controlled variable is probably the most commonly occurring task in data analysis. This procedure is also referred to as linear (or polynomial) regression. Although we have already treated this problem in Sect. 9.4.1, we take it up again here in greater detail. Here we will use different numerical methods, emphasize the most appropriate choice for the order of the polynomial, treat in detail the question of confidence limits, and also give a procedure for the case where the measurement errors are not known.
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© 1999 Springer-Verlag Berlin Heidelberg
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Brandt, S. (1999). Linear and Polynomial Regression. In: Data Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1446-5_12
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DOI: https://doi.org/10.1007/978-1-4612-1446-5_12
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-7147-5
Online ISBN: 978-1-4612-1446-5
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