Why are regression statistics still recommended, given recent publications that emphasize the use of a difference plot as the primary way to present the data from the comparison of methods experiment?
Remember that the purpose of the comparison of methods experiment is to estimate systematic errors, which may be constant or proportional in nature. Regression statistics can provide estimates of these components of systematic error by the y-intercept and slope, as well as estimation of the overall systematic error or bias at any decision level concentration of interest by calculation from the regression equation. The difference plot, on the other hand, emphasizes the random errors between the methods. You actually need to calculate the average difference or bias from paired t-test statistics to get a good estimate of the systematic error, thus the difference plot by itself (without statistical calculations) does not provide sufficient information about the systematic error of the method. Regression statistics are preferred over t-test statistics in order to calculate the systematic error at any decision level, as well as getting estimates of the proportional and constant components of
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