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Proceedings Paper

Role of X-axis uncertainties on standard curves
Author(s): Michael L. Johnson
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Paper Abstract

The commonest method for the determination of standard curves is to use a least-squares technique to fit a function to a set of standard data. This fitted curve is then used to interpolate values from the standard curve. The problem addressed is that the standard data used for this process will usually contain experimental uncertainties in the X-axis (the independent variable) and in the Y-axis (dependent variable). When such X-axis uncertainties exist in the data it is statistically invalid to apply a least-squares procedure to evaluate the coefficients of the standard curve. This statistical invalidity generally cannot be corrected by the application of an `appropriate weighting factor.' However, a simple maximum likelihood procedure can be used to correctly consider the uncertainties in both the X-axis and Y-axis.

Paper Details

Date Published: 3 April 1995
PDF: 7 pages
Proc. SPIE 2386, Ultrasensitive Instrumentation for DNA Sequencing and Biochemical Diagnostics, (3 April 1995); doi: 10.1117/12.206021
Show Author Affiliations
Michael L. Johnson, Univ. of Virginia Health Sciences Ctr. (United States)

Published in SPIE Proceedings Vol. 2386:
Ultrasensitive Instrumentation for DNA Sequencing and Biochemical Diagnostics
Gerald E. Cohn; Jeremy M. Lerner; Kevin J. Liddane; Alexander Scheeline; Steven A. Soper, Editor(s)

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