Articles | Volume 19, issue 19
https://doi.org/10.5194/acp-19-12531-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-12531-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
09 Oct 2019
Technical note |
| 09 Oct 2019
| 09 Oct 2019
Technical note: Effects of uncertainties and number of data points on line fitting – a case study on new particle formation
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
Mikko R. A. Pitkänen
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
Finnish Meteorological Institute, Atmospheric Research Centre of
Eastern Finland, Kuopio, Finland
Tuomo Nieminen
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
currently at: Institute for Atmospheric and Earth System
Research, University of Helsinki, Helsinki, Finland
Antti Lipponen
Finnish Meteorological Institute, Atmospheric Research Centre of
Eastern Finland, Kuopio, Finland
Sini Isokääntä
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
Antti Arola
Finnish Meteorological Institute, Atmospheric Research Centre of
Eastern Finland, Kuopio, Finland
Kari E. J. Lehtinen
Department of Applied Physics, University of Eastern Finland,
Kuopio, Finland
Finnish Meteorological Institute, Atmospheric Research Centre of
Eastern Finland, Kuopio, Finland
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Latest update: 06 May 2025
Short summary
Atmospheric measurement data never come without measurement error. Too often, these errors are neglected when researchers make inferences from their data. We applied multiple line-fitting methods to simulated data mimicking two central variables in aerosol research. Our results show that an ordinary least squares fit, typically used to describe relationships, underestimates the slope of the fit and that methods taking the measurement uncertainty into account performed significantly better.
Atmospheric measurement data never come without measurement error. Too often, these errors are...
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