Orthogonal Regression, Reverse Regression, Heteroscedastic Linear Models and Their Applications in Aerosol Science Presenter: Wei Zhu, SUNY-SB Authors: Ling Leng, Tianyi Zhang, SUNY-SB; Lawrence Kleinman, BNL; and Wei Zhu, SUNY-SB

Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine three regression approaches available to accommodate this situation. They are orthogonal regression, reverse regression and heteroscedastic linear models. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age.