Avoiding some common mistakes in straight line regression. Part 1

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发布日期 2023-11-06
DOI 10.1039/D3AY90134C
影响因子 2.896
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摘要

Analytical scientists generate and use bivariate experimental data mostly in two distinct application areas. In a quantitative calibration experiment standard materials are used to establish a calibration line that can be applied to estimate the concentrations of test samples. A second source of bivariate data is in the comparison of two analytical methods, often for validation purposes: results from the two methods as applied to the same set of test materials are plotted against each other in the hope of obtaining an excellent straight line fit to demonstrate agreement between them. There are several ways of deriving a straight line from bivariate data: the best will depend on which of these applications is involved and on the methods used in assembling the data. In practice a straight line may not be an adequate model for the data, but even if it is, regression methods are often misused and misinterpreted.

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DOI: 10.1039/C9CP90110H

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来源期刊

Analytical Methods

Analytical Methods
CiteScore: 5.1
自引率: 3.7%
年发文量: 655

Analytical Methods welcomes early applications of new analytical and bioanalytical methods and technology demonstrating the potential for societal impact. We require that methods and technology reported in the journal are sufficiently innovative, robust, accurate, and compared to other available methods for the intended application. Developments with interdisciplinary approaches are particularly welcome. Systems should be proven with suitably complex and analytically challenging samples. We encourage developments within, but not limited to, the following technologies and applications: global health, point-of-care and molecular diagnostics biosensors and bioengineering drug development and pharmaceutical analysis applied microfluidics and nanotechnology omics studies, such as proteomics, metabolomics or glycomics environmental, agricultural and food science neuroscience biochemical and clinical analysis forensic analysis industrial process and method development

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