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首页> 外文期刊>The journal of physical chemistry, A. Molecules, spectroscopy, kinetics, environment, & general theory >Fitting Correlated Data: A Critique of the Guggenheim Method and Other Difference Techniques
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Fitting Correlated Data: A Critique of the Guggenheim Method and Other Difference Techniques

机译：拟合相关数据：对古根海姆方法和其他差异技术的评论

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摘要

In certain data treatment procedures, like the Guggenheim method for first-order kinetics data with a background and various combination differences methods in spectroscopy, the analyzed data are obtained by taking differences of the raw data to render the resulting analysis simpler. Such methods can yield correlated data, the proper quantitative analysis of which requires correlated least squares. A formal treatment of these procedures shows that the source of the correlation is not the subtraction itself but the multiple use of data points from the raw data set in producing the differences. Typical applications of the Guggenheim method entail fitting the logarithm of the absolute differences to a straight line. Monte Carlo studies of both a constant-error and a proportional-error model for a declining exponential with a background show that neglect of weights is likely to be a greater source of imprecision than neglect of correlation. The most common form of the method of combination differences does not involve multiple use of the raw data and thus is a statistically sound procedure with no correlation problem.
机译：在某些数据处理程序中，例如带有背景的一阶动力学数据的古根海姆方法和光谱学中的各种组合差异方法，通过获取原始数据的差异来获得分析数据，从而简化了结果分析。此类方法可以产生相关数据，对其进行正确的定量分析需要相关的最小二乘法。对这些程序的正式处理表明，相关性的来源不是减法本身，而是在产生差异时多次使用原始数据集中的数据点。古根海姆方法的典型应用是将绝对差的对数拟合为一条直线。对于具有背景的指数递减的恒定误差和比例误差模型的蒙特卡洛研究表明，权重的忽略比忽略相关性更可能导致不精确。组合差异方法的最常见形式不涉及原始数据的多次使用，因此是一种统计上合理的过程，没有相关性问题。

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