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Linear regression for calibration lines revisited: weighting schemes for bioanalytical methods

机译:再谈校准线的线性回归:生物分析方法的加权方案

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When the assumption of homoscedasticity is not met for analytical data, a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line is to use weighted least squares linear regression (WLSLR). The purpose of the present paper is to stress the relevance of weighting schemes for linear regression analysis and to show how this approach can be useful in the bioanalytical field. The steps to be taken in the study of the linear calibration approach are described. The application of weighting schemes was shown by using a high-performance liquid chromatography method for the determination of lamotrigine in biological fluids as a practical example. By using the WLSLR, the accuracy of the analytical method was improved at the lower end of the calibration curve. Bioanalytical methods data analysis was improved by using the WLSLR procedure.
机译:当分析数据不满足同构假设时,抵消较大浓度对拟合回归线的较大影响的简单有效方法是使用加权最小二乘线性回归(WLSLR)。本文的目的是强调加权方案与线性回归分析的相关性,并说明这种方法如何在生物分析领域有用。描述了线性校准方法研究中要采取的步骤。通过使用高效液相色谱法测定生物液体中拉莫三嗪的实际应用,说明了加权方案的应用。通过使用WLSLR,在校准曲线的下端提高了分析方法的准确性。通过使用WLSLR程序,改进了生物分析方法的数据分析。

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