EFEKTIVITAS METODE NEW STEPWISE DALAM PEMILIHAN VARIABEL PADA MODEL REGRESI GANDA

New stepwise method is a method of selecting predictor variables in a linear reg- ression model. This method is an extension of the principal component regressi- on, and consists of the selection of the original predictor variables iteratively at the same time, a group of main subset component is se...

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Main Author: Thamrin Tayeb
Format: Article
Language:Arabic
Published: Lembaga Pendidikan dan Tenaga Kependidikan (Center for Teacher Training) Faculty of Tarbiyah and Teacher Training Universitas Islam Negeri Alauddin Makassar in collaboration with Lecturer Consortium of Tarbiyah and Teacher Training 2017-12-01
Series:Lentera Pendidikan: Jurnal Ilmu Tarbiyah dan Keguruan
Subjects:
Online Access:http://journal.uin-alauddin.ac.id/index.php/lentera_pendidikan/article/view/3846
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spelling doaj-06a2e0a8d2d2497192653115618cf69d2020-11-25T00:00:35ZaraLembaga Pendidikan dan Tenaga Kependidikan (Center for Teacher Training) Faculty of Tarbiyah and Teacher Training Universitas Islam Negeri Alauddin Makassar in collaboration with Lecturer Consortium of Tarbiyah and Teacher TrainingLentera Pendidikan: Jurnal Ilmu Tarbiyah dan Keguruan1979-34722580-52232017-12-011521611743438EFEKTIVITAS METODE NEW STEPWISE DALAM PEMILIHAN VARIABEL PADA MODEL REGRESI GANDAThamrin Tayeb0Universitas Islam Negeri Alauddin MakassarNew stepwise method is a method of selecting predictor variables in a linear reg- ression model. This method is an extension of the principal component regressi- on, and consists of the selection of the original predictor variables iteratively at the same time, a group of main subset component is selected repeatedly. This me- thod has also the basic properties of the stepwise method. Thus we will get the best combination of stepwise selection and principal component selection me- thods. Model that is obtained by using this method characterizes a low-valued PRESS. The application of this method is not only for linear model, but also can  be expanded to generalized linear models. The comparison of both methods are based on the R2 criteria in the variable selection, obtained R2 value results which are almost the same as those models in the case of solid waste of data, so having payed fully attention to the number of predictor variables entered into the mo- dels, it can be said that the new stepwise method tends to be better than the prin- cipal component regression.http://journal.uin-alauddin.ac.id/index.php/lentera_pendidikan/article/view/3846Multikolinearitaspemilihan variabelkomponen utama
collection DOAJ
language Arabic
format Article
sources DOAJ
author Thamrin Tayeb
spellingShingle Thamrin Tayeb
EFEKTIVITAS METODE NEW STEPWISE DALAM PEMILIHAN VARIABEL PADA MODEL REGRESI GANDA
Lentera Pendidikan: Jurnal Ilmu Tarbiyah dan Keguruan
Multikolinearitas
pemilihan variabel
komponen utama
author_facet Thamrin Tayeb
author_sort Thamrin Tayeb
title EFEKTIVITAS METODE NEW STEPWISE DALAM PEMILIHAN VARIABEL PADA MODEL REGRESI GANDA
title_short EFEKTIVITAS METODE NEW STEPWISE DALAM PEMILIHAN VARIABEL PADA MODEL REGRESI GANDA
title_full EFEKTIVITAS METODE NEW STEPWISE DALAM PEMILIHAN VARIABEL PADA MODEL REGRESI GANDA
title_fullStr EFEKTIVITAS METODE NEW STEPWISE DALAM PEMILIHAN VARIABEL PADA MODEL REGRESI GANDA
title_full_unstemmed EFEKTIVITAS METODE NEW STEPWISE DALAM PEMILIHAN VARIABEL PADA MODEL REGRESI GANDA
title_sort efektivitas metode new stepwise dalam pemilihan variabel pada model regresi ganda
publisher Lembaga Pendidikan dan Tenaga Kependidikan (Center for Teacher Training) Faculty of Tarbiyah and Teacher Training Universitas Islam Negeri Alauddin Makassar in collaboration with Lecturer Consortium of Tarbiyah and Teacher Training
series Lentera Pendidikan: Jurnal Ilmu Tarbiyah dan Keguruan
issn 1979-3472
2580-5223
publishDate 2017-12-01
description New stepwise method is a method of selecting predictor variables in a linear reg- ression model. This method is an extension of the principal component regressi- on, and consists of the selection of the original predictor variables iteratively at the same time, a group of main subset component is selected repeatedly. This me- thod has also the basic properties of the stepwise method. Thus we will get the best combination of stepwise selection and principal component selection me- thods. Model that is obtained by using this method characterizes a low-valued PRESS. The application of this method is not only for linear model, but also can  be expanded to generalized linear models. The comparison of both methods are based on the R2 criteria in the variable selection, obtained R2 value results which are almost the same as those models in the case of solid waste of data, so having payed fully attention to the number of predictor variables entered into the mo- dels, it can be said that the new stepwise method tends to be better than the prin- cipal component regression.
topic Multikolinearitas
pemilihan variabel
komponen utama
url http://journal.uin-alauddin.ac.id/index.php/lentera_pendidikan/article/view/3846
work_keys_str_mv AT thamrintayeb efektivitasmetodenewstepwisedalampemilihanvariabelpadamodelregresiganda
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