Modeling of Human Development Index Using Ridge Regression Method

  • Ferra Yanuar Mathematics Department, FMIPA, Universitas Andalas, Indonesia
  • Mardha Tillah Mathematics Department, FMIPA, Universitas Andalas, Indonesia
  • Dodi Devianto Mathematics Department, FMIPA, Universitas Andalas, Indonesia
Keywords: Modelling, HDI, Index, Ridge Regression

Abstract

This article aims to model factors affecting HDI (Human Development Index) in North Sumatera by 2015 using ridge regression method. This ridge regression method is used because in the IPM data there is a multicolinearity problem so that the least squares regression method, as regression method commonly used in statistical modeling, is not suitable for use any more. This study compares the models resulting from the use of the least squares method and the ridge regression method to the HDI data. This study proves that the ridge regression method produces a better model and can eliminate the multicolinearity effect, while the least squares method can not. The significant factors in affecting HDI on North Sumtera data in 2015 are Average School length and Total expenditure / capita / month. The indicator of the goodness of this ridge regression model is 81.81% which means that the model is good and could be accepted.

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Published
2018-10-30
How to Cite
1.
Yanuar F, Tillah M, Devianto D. Modeling of Human Development Index Using Ridge Regression Method. EKSAKTA [Internet]. 30Oct.2018 [cited 26Mar.2019];19(2):1-1. Available from: http://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/134

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