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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/acp-2018-1125
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-1125
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Technical note 11 Dec 2018

Technical note | 11 Dec 2018

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Chemistry and Physics (ACP).

Technical note: Effects of Uncertainties and Number of Data points on Inference from Data – a Case Study on New Particle Formation

Santtu Mikkonen1, Mikko R. A. Pitkänen1,2, Tuomo Nieminen1, Antti Lipponen2, Sini Isokääntä1, Antti Arola2, and Kari E. J. Lehtinen1,2 Santtu Mikkonen et al.
  • 1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
  • 2Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland

Abstract. Fitting a line on a scatterplot of two measured variables is considered as one of the simplest statistical procedures researchers can do. However, this simplicity is deceptive as the line fitting procedure is actually quite a complex problem. Atmospheric measurement data never comes without some measurement error. Too often, these errors are neglected when researchers are making inferences from their data.

To demonstrate the problem, we simulated datasets with different amounts of data and error, mimicking the dependence of atmospheric new particle formation rate (J1.7) on sulphuric acid concentration (H2SO4). Both variables have substantial measurement error and thus they are good test variables for our study. We show that ordinary least squares (OLS) regression results in strongly biased slope values compared with six error-in-variables (EIV) regression methods (Deming, Principal component analysis, orthogonal, Bayesian EIV, and two different bivariate regression methods) known to take into account errors in the variables.

Santtu Mikkonen et al.
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Short summary
Atmospheric measurement data never comes without measurement error. Too often, these errors are neglected, when researchers are making inferences from their data. We applied multiple line fitting methods to simulated data mimicking two central variables in aerosol research. Our results show, that ordinary least squares fit, typically used to describe the relationships, underestimates the slope of the fit and methods taking account the measurement uncertainty performed significantly better.
Atmospheric measurement data never comes without measurement error. Too often, these errors are...
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