Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data

Multiple linear regressions (MLR) model is an important tool for investigating relationships between several response variables and some predictor variables. This method is very powerful and commonly used in finance, economic, medical, agriculture and many more. The main objective of this paper is t...

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Main Authors: Awang Nawi, Mohamad Arif, Wan Ahmad, Wan Muhamad Amir, Mohd Ibrahim, Mohamad Shafiq, Mamat, Mustafa, Khamis, Mohd Fahdli, Mohamed, Mohamad Afendee
Format: Article
Language:English
English
Published: Blue Eyes Intelligence Engineering and Sciences Publication 2019
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Online Access:http://irep.iium.edu.my/79771/1/79771_Proving%20the%20efficiency%20of%20alternative.pdf
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spelling my.iium.irep.797712020-03-24T03:55:08Z http://irep.iium.edu.my/79771/ Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data Awang Nawi, Mohamad Arif Wan Ahmad, Wan Muhamad Amir Mohd Ibrahim, Mohamad Shafiq Mamat, Mustafa Khamis, Mohd Fahdli Mohamed, Mohamad Afendee RK Dentistry Multiple linear regressions (MLR) model is an important tool for investigating relationships between several response variables and some predictor variables. This method is very powerful and commonly used in finance, economic, medical, agriculture and many more. The main objective of this paper is to compare mean square error (MSE) and the average width between alternative linear regression models and linear regression model. The alternative method in this study is a combination of four methods, namely multiple linear regression method, the bootstrap method, a robust regression method and fuzzy regression through the construction of algorithms by using SAS software. Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. The results of the study showed a positive improvement for the estimation of parameters generated through these alternative methods. © BEIESP. Blue Eyes Intelligence Engineering and Sciences Publication 2019-07 Article PeerReviewed application/pdf en http://irep.iium.edu.my/79771/1/79771_Proving%20the%20efficiency%20of%20alternative.pdf application/pdf en http://irep.iium.edu.my/79771/2/79771_Proving%20the%20efficiency%20of%20alternative_SCOPUS.pdf Awang Nawi, Mohamad Arif and Wan Ahmad, Wan Muhamad Amir and Mohd Ibrahim, Mohamad Shafiq and Mamat, Mustafa and Khamis, Mohd Fahdli and Mohamed, Mohamad Afendee (2019) Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data. International Journal of Recent Technology and Engineering, 8 (2, Special issue 2). pp. 377-381. ISSN 2277-3878 https://www.ijrte.org/wp-content/uploads/papers/v8i2S3/B10650782S319.pdf 10.35940/ijrte.B1065.0782S319
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic RK Dentistry
spellingShingle RK Dentistry
Awang Nawi, Mohamad Arif
Wan Ahmad, Wan Muhamad Amir
Mohd Ibrahim, Mohamad Shafiq
Mamat, Mustafa
Khamis, Mohd Fahdli
Mohamed, Mohamad Afendee
Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data
description Multiple linear regressions (MLR) model is an important tool for investigating relationships between several response variables and some predictor variables. This method is very powerful and commonly used in finance, economic, medical, agriculture and many more. The main objective of this paper is to compare mean square error (MSE) and the average width between alternative linear regression models and linear regression model. The alternative method in this study is a combination of four methods, namely multiple linear regression method, the bootstrap method, a robust regression method and fuzzy regression through the construction of algorithms by using SAS software. Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. The results of the study showed a positive improvement for the estimation of parameters generated through these alternative methods. © BEIESP.
format Article
author Awang Nawi, Mohamad Arif
Wan Ahmad, Wan Muhamad Amir
Mohd Ibrahim, Mohamad Shafiq
Mamat, Mustafa
Khamis, Mohd Fahdli
Mohamed, Mohamad Afendee
author_facet Awang Nawi, Mohamad Arif
Wan Ahmad, Wan Muhamad Amir
Mohd Ibrahim, Mohamad Shafiq
Mamat, Mustafa
Khamis, Mohd Fahdli
Mohamed, Mohamad Afendee
author_sort Awang Nawi, Mohamad Arif
title Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data
title_short Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data
title_full Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data
title_fullStr Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data
title_full_unstemmed Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data
title_sort proving the efficiency of alternative linear regression model based on mean square error (mse) and average width using aquaculture data
publisher Blue Eyes Intelligence Engineering and Sciences Publication
publishDate 2019
url http://irep.iium.edu.my/79771/1/79771_Proving%20the%20efficiency%20of%20alternative.pdf
http://irep.iium.edu.my/79771/2/79771_Proving%20the%20efficiency%20of%20alternative_SCOPUS.pdf
http://irep.iium.edu.my/79771/
https://www.ijrte.org/wp-content/uploads/papers/v8i2S3/B10650782S319.pdf
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score 13.211869