Pemodelan Regresi Weibull Pada Data Kontinu Yang Diklasifikasikan (Studi kasus: Data Indikator Pencemaran Air Dissolved Oxygen  Pada DAS Mahakam Kalimantan Timur Tahun 2020)

Alfiannur Rizki Sudarman; Suyitno Suyitno; Meiliyani Siringoringo

doi:10.30872/eksponensial.v14i1.993

Alfiannur Rizki Sudarman Universitas Mulawarman
Suyitno Suyitno Laboratorium Statistika Terapan Fakultas MIPA, Universitas Mulawarman
Meiliyani Siringoringo Laboratorium Statistika Ekonomi Bisnis, Fakultas MIPA, Universitas Mulawarman

DOI: https://doi.org/10.30872/eksponensial.v14i1.993

Abstract

Weibull regression model is a Weibull distribution that is directly influenced by covariates. Weibull regression models discussed in this study are Weibull survival regression model, Weibull hazard regression, and Weibull mean regression. The Weibull regression model in this study was applied to water pollution indicator of dissolved oxygen (DO) data in the Mahakam watershed of East Kalimantan in 2020. The purpose of this study was to obtain a Weibull regression model for water pollution indicator of DO data, to obtain the factors that influence the Weibull regression model, and to interpretation the Weibull regression model of water pollution indicator of DO data. The study’s result is that the Newton-Raphson iterative approach was used to find the approximate of maximum likelihood estimator. Based on the hypothesis testing, it is concluded the factors that influence the water pollution indicator of DO data the Mahakam watershed in 2020 are total suspended solid (TSS), total dissolved solid (TDS), nitrate and ammonia.

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