Review on circular-linear regression models

Classical linear statistics method is no longer appropriate when handling circular data since the data is influenced by direction or angle. Considering the possibility of circular data appeared as dependent variable, it has resulted in the remodeling of classic linear regression model into circular-...

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Main Authors: Hanin Hazwani, Mohammad, Siti Zanariah, Satari, Wan Nur Syahidah, Wan Yusoff
Format: Conference or Workshop Item
Language:English
Published: IOP Publishing Ltd 2021
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Online Access:http://umpir.ump.edu.my/id/eprint/35391/1/Review%20on%20circular-linear%20regression%20models.pdf
http://umpir.ump.edu.my/id/eprint/35391/
https://doi.org/10.1088/1742-6596/1988/1/012108
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spelling my.ump.umpir.353912022-10-26T01:55:15Z http://umpir.ump.edu.my/id/eprint/35391/ Review on circular-linear regression models Hanin Hazwani, Mohammad Siti Zanariah, Satari Wan Nur Syahidah, Wan Yusoff Q Science (General) QA Mathematics T Technology (General) Classical linear statistics method is no longer appropriate when handling circular data since the data is influenced by direction or angle. Considering the possibility of circular data appeared as dependent variable, it has resulted in the remodeling of classic linear regression model into circular-linear regression model over the past few decades. It is important to acknowledge these circular data characteristics as it can affect the descriptive and inference of statistical analysis. With the growing body of literature regarding this issue, this paper will review on circular-linear regression model by highlighting and exploring their benefits and limitations. IOP Publishing Ltd 2021-08-17 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/35391/1/Review%20on%20circular-linear%20regression%20models.pdf Hanin Hazwani, Mohammad and Siti Zanariah, Satari and Wan Nur Syahidah, Wan Yusoff (2021) Review on circular-linear regression models. In: Journal of Physics: Conference Series, Simposium Kebangsaan Sains Matematik ke-28 (SKSM28), 28-29 July 2021 , Kuantan, Pahang. pp. 1-12., 1988. ISSN 1742-6588 https://doi.org/10.1088/1742-6596/1988/1/012108
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
QA Mathematics
T Technology (General)
spellingShingle Q Science (General)
QA Mathematics
T Technology (General)
Hanin Hazwani, Mohammad
Siti Zanariah, Satari
Wan Nur Syahidah, Wan Yusoff
Review on circular-linear regression models
description Classical linear statistics method is no longer appropriate when handling circular data since the data is influenced by direction or angle. Considering the possibility of circular data appeared as dependent variable, it has resulted in the remodeling of classic linear regression model into circular-linear regression model over the past few decades. It is important to acknowledge these circular data characteristics as it can affect the descriptive and inference of statistical analysis. With the growing body of literature regarding this issue, this paper will review on circular-linear regression model by highlighting and exploring their benefits and limitations.
format Conference or Workshop Item
author Hanin Hazwani, Mohammad
Siti Zanariah, Satari
Wan Nur Syahidah, Wan Yusoff
author_facet Hanin Hazwani, Mohammad
Siti Zanariah, Satari
Wan Nur Syahidah, Wan Yusoff
author_sort Hanin Hazwani, Mohammad
title Review on circular-linear regression models
title_short Review on circular-linear regression models
title_full Review on circular-linear regression models
title_fullStr Review on circular-linear regression models
title_full_unstemmed Review on circular-linear regression models
title_sort review on circular-linear regression models
publisher IOP Publishing Ltd
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/35391/1/Review%20on%20circular-linear%20regression%20models.pdf
http://umpir.ump.edu.my/id/eprint/35391/
https://doi.org/10.1088/1742-6596/1988/1/012108
_version_ 1748180696544837632
score 13.160551