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Johnson ML. Why, when, and how biochemists should use least squares. Anal Biochem. 1992;206(2):215-25doi: 10.1016/0003-2697(92)90356-c.
Johnson, M. L. (1992). Why, when, and how biochemists should use least squares. Analytical biochemistry, 206(2), 215-25. https://doi.org/10.1016/0003-2697(92)90356-c
Johnson, M L. "Why, when, and how biochemists should use least squares." Analytical biochemistry vol. 206,2 (1992): 215-25. doi: https://doi.org/10.1016/0003-2697(92)90356-c
Johnson ML. Why, when, and how biochemists should use least squares. Anal Biochem. 1992 Nov 01;206(2):215-25. doi: 10.1016/0003-2697(92)90356-c. PMID: 1443589.
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Elsevier Science

Anal Biochem. 1992 Nov 01;206(2):215-25. doi: 10.1016/0003-2697(92)90356-c.

Why, when, and how biochemists should use least squares.

Analytical biochemistry

M L Johnson

Affiliations

  1. Department of Pharmacology, University of Virginia Health Sciences Center, Charlottesville 22908.

PMID: 1443589 DOI: 10.1016/0003-2697(92)90356-c

Abstract

One of the most commonly used methods for the analysis of experimental data in the biochemical literature is nonlinear least squares (regression). This group of methods are also commonly misused. The purpose of this article is to review the assumptions inherent in the use of least-squares techniques and how these assumptions govern the ways that least-squares techniques can and should be used. Since these assumptions pertain to the nature of the experimental data to be analyzed they also dictate many aspects of the data collection protocol. The examination of these assumptions includes a discussion of questions like: Why would a biochemist want to use nonlinear least-squares techniques? When is it appropriate for a biochemist to use nonlinear least-squares techniques? What confidence can be assigned to the results of a nonlinear least-squares analysis?

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