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All Journal Pattimura Proceeding : Conference of Science and Technology Journal of Statistics and Data Science
Wahyuliani, Indah
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Binary Logistic Regression Modeling on Household Poverty Status in Bengkulu Province Sihombing, Esther Damayanti; Novianti, Pepi; Wahyuliani, Indah
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p89-100

Abstract

Binary logistic regression is a statistical method used to analyze the relationship between one or more predictor variables and a binary or dichotomous response variable. Poverty is an issue in every province in Indonesia. One of the provinces with a relatively high poverty rate is Bengkulu Province, ranking seventh in Indonesia with a poverty rate of 14.62%. The Central Bureau of Statistics of Bengkulu Province (2023) explains that efforts to reduce poverty must involve all levels of society. Various government programs and policies in various fields such as health, social, and other areas are continuously being implemented to reduce the number of households classified as poor. Identifying the characteristics of households in Bengkulu Province by poverty status is important to study, as it serves as a reference to ensure that government programs are implemented according to the target. One method that can be used to identify household characteristics is binary logistic regression. This study aims to model the poverty status of households in Bengkulu Province using binary logistic regression and to identify the factors that influence it. The data used are social and economic household data from March 2022. The response variable used is household poverty status (poor and not poor), while the predictor variables include the ownership of toilet facilities, the source of lighting, floor area, family size, and per capita calorie consumption. Modeling is done using binary logistic regression with simultaneous and partial parameter significance tests, as well as model fit tests. The analysis results show that the factors significantly influencing household poverty status in Bengkulu Province are the ownership of toilet facilities, the source of household lighting, floor area, family size, and per capita calorie consumption. The formed binary logistic regression model has a classification accuracy of 89.98% with a sensitivity of 18.34% and a specificity of 98.61%.
An Analysis of Factors Contributing to Extended Study Duration Among Students of the Faculty of Mathematics and Natural Sciences, University of Bengkulu Using Binary Logistic Regression Wahyuliani, Indah; Novianti, Pepi
Journal of Statistics and Data Science Vol. 2 No. 2 (2023)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v2i2.41287

Abstract

Logistic regression is a statistical method used to analyze the relationship between a dichotomous dependent variable and one or more independent variables, which may be numerical or categorical. In this study, binary logistic regression is applied to identify the factors influencing the study duration of students in the Faculty of Mathematics and Natural Sciences at the University of Bengkulu. These factors include both internal and external elements, such as cumulative GPA (Grade Point Average), gender, parents’ occupation, scholarship status, and university admission pathway. The results show that GPA significantly affects the length of study, with an odds ratio of 1102.13, indicating that each one-unit increase in GPA greatly increases the likelihood of graduating on time. This study suggests the use of additional statistical techniques, such as bootstrapping, to enhance parameter estimation accuracy and recommends reporting effect sizes, such as odds ratios, for a more comprehensive interpretation of the relationship between independent and dependent variables.
Co-Authors Pepi Novianti Sihombing, Esther Damayanti