Comment on regression analysis of air quality data

By G. Ayers. 2001

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Surface ozone-temperature relationships in the eastern US: A monthly climatology for evaluating chemistry-climate models (2011, methodology): “presence of measurement uncertainties in both x- and y- variables (Ayers, 2001; Cantrell, 2008; Davis, 1986).”
Quantitative comparisons and models of time-averaging in bivalve and brachiopod shell accumulations (2010, result): “Although the two fitting methods yield very similar results in this case (data not shown), reduced major axis is more appropriate because both the x and y variables have errors associated with them (Griffiths 1998; Ikemoto and Takai 2000; Ayers 2001).”
Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting (2017, methodology): “Ayers (2001) conducted a simple numerical experiment and concluded that reduced major axis regression (RMA) is more suitable for air quality data regression analysis. Linnet (1999) pointed out that when applying DR for inter-method (or interinstrument) comparison, special attention should be paid to”