PENDEKATAN REGRESI SPLINE UNTUK MEMODELKAN POLA PERTUMBUHAN BERAT BADAN BALITA
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Authors:
NI LUH SUKERNI, I KOMANG GDE SUKARSA, NI LUH PUTU SUCIPTAWATI
Abstract:
“The study is aimed to estimate the best spline regression model for toddler’s weight growth patterns. Spline is one of the nonparametric regression estimation method which has a high flexibility and is able to handle data that change in particular subintervals so thus resulting in model which fitted the data. This study uses data of toddler’s weight growth at Posyandu Mekar Sari, Desa Suwug, Kabupaten Buleleng. The best spline regression model is chosen based on the minimum Generalized Cross Validation (GCV) value. The study shows that the best spline regression model for the data is quadratic spline regression model with six optimal knot points. The minimum GCV value is 0,900683471925 with the determination coefficient equals to 0,954609.”
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PDF:
https://jurnal.harianregional.com/mtk/full-41903
Published
2018-09-02
How To Cite
SUKERNI, NI LUH; SUKARSA, I KOMANG GDE; SUCIPTAWATI, NI LUH PUTU. PENDEKATAN REGRESI SPLINE UNTUK MEMODELKAN POLA PERTUMBUHAN BERAT BADAN BALITA.E-Jurnal Matematika, [S.l.], v. 7, n. 3, p. 259-263, sep. 2018. ISSN 2303-1751. Available at: https://jurnal.harianregional.com/mtk/id-41903. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/MTK.2018.v07.i03.p212.
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Issue
Vol 7 No 3 (2018)
Section
Articles
Copyright
This work is licensed under a Creative Commons Attribution 4.0 International License
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