Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka

Multivariate Adaptive Regression Splines (MARS) used to model the active student’s status in the Department of Statistics at Universitas Terbuka and determine the factors that influence the response variable. This study consists of 9 variables, namely gender, age, education, marital status, job, ini...

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Autor principal:	Siti Hadijah Hasanah
Formato:	article
Lenguaje:	EN
Publicado:	Department of Mathematics, UIN Sunan Ampel Surabaya 2021
Materias:	basis function gcv multivariate recursive splines Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/357c7a2085064d61a7a741d74eb16a41
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spelling	oai:doaj.org-article:357c7a2085064d61a7a741d74eb16a412021-12-02T17:28:36ZMultivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka2527-31592527-316710.15642/mantik.2021.7.1.51-58https://doaj.org/article/357c7a2085064d61a7a741d74eb16a412021-05-01T00:00:00Zhttp://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/1109https://doaj.org/toc/2527-3159https://doaj.org/toc/2527-3167Multivariate Adaptive Regression Splines (MARS) used to model the active student’s status in the Department of Statistics at Universitas Terbuka and determine the factors that influence the response variable. This study consists of 9 variables, namely gender, age, education, marital status, job, initial registration year, number of registrations, credits, and GPA, but after modeling using the MARS method, the explanatory variable can affect the response variable is the initial registration year. Several registrations, GPA, and credits. Based on the results of the R output and using a 95% confidence interval, each base 1 to 10 function is partially significant with the p-value of the base 1-10 function being smaller than 0.05 and simultaneously with a smaller p-value. of 0.05, so that the above model has a significant effect partially or simultaneously on the response variable. From these results, it is concluded that the MARS model is suitable for determining the factors that affect the active status of students.Siti Hadijah HasanahDepartment of Mathematics, UIN Sunan Ampel Surabayaarticlebasis functiongcvmultivariaterecursivesplinesMathematicsQA1-939ENMantik: Jurnal Matematika, Vol 7, Iss 1, Pp 51-58 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	basis function gcv multivariate recursive splines Mathematics QA1-939
spellingShingle	basis function gcv multivariate recursive splines Mathematics QA1-939 Siti Hadijah Hasanah Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka
description	Multivariate Adaptive Regression Splines (MARS) used to model the active student’s status in the Department of Statistics at Universitas Terbuka and determine the factors that influence the response variable. This study consists of 9 variables, namely gender, age, education, marital status, job, initial registration year, number of registrations, credits, and GPA, but after modeling using the MARS method, the explanatory variable can affect the response variable is the initial registration year. Several registrations, GPA, and credits. Based on the results of the R output and using a 95% confidence interval, each base 1 to 10 function is partially significant with the p-value of the base 1-10 function being smaller than 0.05 and simultaneously with a smaller p-value. of 0.05, so that the above model has a significant effect partially or simultaneously on the response variable. From these results, it is concluded that the MARS model is suitable for determining the factors that affect the active status of students.
format	article
author	Siti Hadijah Hasanah
author_facet	Siti Hadijah Hasanah
author_sort	Siti Hadijah Hasanah
title	Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka
title_short	Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka
title_full	Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka
title_fullStr	Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka
title_full_unstemmed	Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka
title_sort	multivariate adaptive regression splines (mars) for modeling student status at universitas terbuka
publisher	Department of Mathematics, UIN Sunan Ampel Surabaya
publishDate	2021
url	https://doaj.org/article/357c7a2085064d61a7a741d74eb16a41
work_keys_str_mv	AT sitihadijahhasanah multivariateadaptiveregressionsplinesmarsformodelingstudentstatusatuniversitasterbuka
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Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka

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