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Penerapan Metode Linearized Ridge Regression pada Data yang Mengandung Multikolinearitas

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 1 (2023) · pp. 122–129

DOI
10.20956/ejsa.vi.19081
ISSN
2721-379X, 2721-3803
Published
2023-02-14
Publisher
Hasanuddin University
Language
en

Abstract

One of the assumptions that must be met in the multiple linear regression model is that there is no multicollinearity problem among the independent variables. However, if there is a multicollinearity problem, then parameter estimation can be done using the linearized ridge regression (LRR) method. The LRR method has the advantage of choosing an optimal constant that is easy to determine and also has a minimum PRESS value. In this study, the infant mortality rate in South Sulawesi Province will be modeled using the LRR method based on the variables of the amount of vitamin A given, the number of health services, the number of babies born with low weight, the number of mothers who give birth assisted by medical personnel, and the number of babies who are breastfed. exclusive. One measure to see the goodness of the regression model is the Prediction Error Sum of Squares (PRESS). Based on the t-test at a significance level of 5%, the total coverage of vitamin A administration and the number of babies born with low weight gave a significant effect on infant mortality with a PRESS value of 0.6846.

Keywords

Collaboration network of the authors

37 authors · 98 co-authorship links

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  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
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Co-authorOrganisationJoint articles
Erna Tri HerdianiAffiliation unknown4
Georgina Maria TinungkiAffiliation unknown3
Andi Kresna JayaAffiliation unknown3
La Podje TalangkoAffiliation unknown2
Raupong RaupongAffiliation unknown2
Iis Cendrah KasihAffiliation unknown1
Rizki AdiputraAffiliation unknown1
Risma RismaAffiliation unknown1
Dewi Santika Upa P.Affiliation unknown1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Reski AmalahAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Ratmila MammiAffiliation unknown1
Nur Alfianingsih RajaAffiliation unknown1
Anisa KalondengAffiliation unknown1
Nurkamalia NurkamaliaAffiliation unknown1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Hendriete Tiur Marowi WaibusiAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Nirwan NirwanAffiliation unknown1
Nurhidayatullah NurhidayatullahAffiliation unknown1
Nuralyatussa’ adaAffiliation unknown1
Nurul HikmahInstitut Agama Islam Negeri Kendari1
Parida Ayu NingsiAffiliation unknown1
Amalia AndrianingrumAffiliation unknown1
Siswanto SiswantoAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Muhammad FadilAffiliation unknown1
A.Muthiah Nur AngrianyAffiliation unknown1
Rini JamalAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Penerapan Metode Linearized Ridge Regression pada Data yang Mengandung Multikolinearitas</dc:title>
	<dc:creator xml:lang="en">Adam, Mukrimin</dc:creator>
	<dc:creator xml:lang="en">Sahriman, Sitti</dc:creator>
	<dc:creator xml:lang="en">Sirajang, Nasrah</dc:creator>
	<dc:subject xml:lang="en">Infant Mortality Rate</dc:subject>
	<dc:subject xml:lang="en">Multicollinearity</dc:subject>
	<dc:subject xml:lang="en">Linearized Ridge Regression</dc:subject>
	<dc:subject xml:lang="en">Prediction Error Sum of Squares</dc:subject>
	<dc:description xml:lang="en">One of the assumptions that must be met in the multiple linear regression model is that there is no multicollinearity problem among the independent variables. However, if there is a multicollinearity problem, then parameter estimation can be done using the linearized ridge regression (LRR) method. The LRR method has the advantage of choosing an optimal constant that is easy to determine and also has a minimum PRESS value. In this study, the infant mortality rate in South Sulawesi Province will be modeled using the LRR method based on the variables of the amount of vitamin A given, the number of health services, the number of babies born with low weight, the number of mothers who give birth assisted by medical personnel, and the number of babies who are breastfed. exclusive. One measure to see the goodness of the regression model is the Prediction Error Sum of Squares (PRESS). Based on the t-test at a significance level of 5%, the total coverage of vitamin A administration and the number of babies born with low weight gave a significant effect on infant mortality with a PRESS value of 0.6846.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2023-02-14</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/19081</dc:identifier>
	<dc:identifier>10.20956/ejsa.vi.19081</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 1, Januari, 2023 : Estimasi; 122-129</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/19081/9339</dc:relation>
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