Articles | Volume 11, issue 2
Atmos. Meas. Tech., 11, 1233–1250, 2018
https://doi.org/10.5194/amt-11-1233-2018
Atmos. Meas. Tech., 11, 1233–1250, 2018
https://doi.org/10.5194/amt-11-1233-2018

Research article 02 Mar 2018

Research article | 02 Mar 2018

Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

Cheng Wu and Jian Zhen Yu

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Cheng Wu on behalf of the Authors (20 Dec 2017)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (05 Jan 2018) by Willy Maenhaut
AR by Cheng Wu on behalf of the Authors (11 Jan 2018)  Author's response
ED: Publish subject to minor revisions (review by editor) (19 Jan 2018) by Willy Maenhaut
AR by Cheng Wu on behalf of the Authors (21 Jan 2018)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (05 Feb 2018) by Willy Maenhaut
AR by Cheng Wu on behalf of the Authors (06 Feb 2018)  Author's response    Manuscript
ED: Publish as is (06 Feb 2018) by Willy Maenhaut
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Short summary
A new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator is proposed to conduct benchmark tests on a variety of linear regression techniques. With an appropriate weighting, Deming regression (DR), weighted ODR (WODR), and York regression (YR) are recommended for atmospheric studies when both x and y data have measurement errors. An Igor-based program (Scatter Plot) is developed to facilitate the regression implementation.