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Abstract

The linear regression model is employed when it is identified a linear relationship between the dependent and independent variables. In some cases, the relationship between the two variables does not generate a linear line, that is, there is a change point at a certain point. Therefore, the
maximum likelihood estimator for the linear regression does not produce an accurate model. The objective of this study is to presents the performance of simple linear and segmented linear regression models in which there are breakpoints in the data. The modeling is performed on
the data of depth and sea temperature. The model results display that the segmented linear regression is better in modeling data which contain changing points than the classical one.

Received September 1, 2021
Revised November 2, 2021
Accepted November 11, 2021

Keywords

Linear Regression Maximum likelihood Breakpoint Piecewise model Segmented regression

Article Details

Author Biographies

Muhammad Bayu Nirwana, Universitas Sebelas Maret, Indonesia

 

 

Dewi Wulandari, Universitas PGRI Semarang, Indonesia

 

 

How to Cite
Nirwana, M. B., & Wulandari, D. (2021). Comparison of Simple and Segmented Linear Regression Models on the Effect of Sea Depth toward the Sea Temperature. Enthusiastic : International Journal of Applied Statistics and Data Science, 1(2), 68–75. https://doi.org/10.20885/enthusiastic.vol1.iss2.art3
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