Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)

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Multivariable Truncated Spline Nonparametric Regression in Modeling Human Development Index (HDI) of Southeast Sulawesi Province

Authors
I. Nyoman Sudiasa1, *, Nurjeni Yanti2, BaharUddin2, Andi Tenri Ampa2, Makkulau2, Agusrawati2, Andi Tenri Panngangareng Makkulau2
1Civil Engineering Department, Institut Teknologi Nasional Malang, Malang, Indonesia
2Statistics Department, Universitas Halu Oleo, Kendari, Indonesia
*Corresponding author.
Corresponding Author
I. Nyoman Sudiasa
Available Online 18 December 2023.
DOI
10.2991/978-94-6463-332-0_10How to use a DOI?
Keywords
Spline Truncated Nonparametric Regression; HDI; knot point; GCV
Abstract

The aim of this research was to determine the modelling of human development index (HDI) data of Southeast Sulawesi Province using the Spline Truncated Nonparametric Regression analysis method and also to determine the factors that influence it. Spline regression is able to overcome data patterns that show sharp increases or decreases with the help of knot points, and the resulting curve is relatively smooth. The data used in this study is the Human Development Index (HDI) data in 2022 along with the factors that are thought to affect it obtained from the Central Statistics Agency (BPS). Based on the test results that have been carried out, the best model using spline truncated nonparametric regression for the human development index (HDI) of Southeast Sulawesi Province is a model with three knot points with a minimum generalised cross validation (GCV) value of 5,93. The predictor variables used, namely the labour force participation rate (X1), school enrolment rate (X2), and morbidity rate (X3) have a significant effect on the human development index (HDI) in Southeast Sulawesi Province with a coefficient determination (R2) of 72,66%.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
Series
Advances in Computer Science Research
Publication Date
18 December 2023
ISBN
10.2991/978-94-6463-332-0_10
ISSN
2352-538X
DOI
10.2991/978-94-6463-332-0_10How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - I. Nyoman Sudiasa
AU  - Nurjeni Yanti
AU  - BaharUddin
AU  - Andi Tenri Ampa
AU  - Makkulau
AU  - Agusrawati
AU  - Andi Tenri Panngangareng Makkulau
PY  - 2023
DA  - 2023/12/18
TI  - Multivariable Truncated Spline Nonparametric Regression in Modeling Human Development Index (HDI) of Southeast Sulawesi Province
BT  - Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
PB  - Atlantis Press
SP  - 77
EP  - 86
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-332-0_10
DO  - 10.2991/978-94-6463-332-0_10
ID  - Sudiasa2023
ER  -