Count Regression Models for Analyzing Crime Rates in The East Java Province

Handayani, Dian; Artari, A F; Safitri, W; Rahayu, Wardani; Santi, Vera Maya

doi:10.1088/1742-6596/2123/1/012028

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Model regresi poisson adalah salah satu kasus khusus pada Generalized Linear Model (GLM) yang umumnya digunakan untuk memodelkan hubungan antar variabel respons cacahan dan beberapa variabel penjelas berupa data diskrit, kontinu, kategorik, atau campuran (Handayani et al, 2021;Ruliana et al, 2016). Dalam kasus dimana data variabel penjelas adalah kategorik, pada umumnya data akan dimodelkan menjadi jumlah sel dari tabel klasifikasi silang atau tabel kontingensi.…”

Section: Pendahuluanunclassified

Log Linier Binomial Negatif dalam Memodelkan Data Siswa Putus Sekolah Tingkat SMA/SMK

Santi,

Fanya Izmi Hawa,

Bagus Sumargo

2023

JSA

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Log linear negative binomial model is an extension of the linear regression model that can be used to analyze count data in the form of a contingency table when dealing with overdispersion where the variance value is greater than the mean value. Research in the field of education often produces contingency table data, one of which is data on students dropping out of school. The 2018 BPS survey showed that the number of students dropping out of school at the secondary school level in West Java province had the highest number of dropouts. Unfortunately, quantitative research on dropout students is still rarely done. Log linear negative binomial model is applied to determine the factors that influence the number of SMA/SMK dropout students in West Java province in 2021. The results show that gender, type of middle school, school status, interaction of gender and school status, and the interaction of middle school type and school status significantly affects the number of students dropping out of school. Furthermore, SMA/SMK dropouts in the province of West Java are dominated by male students, vocational students, and public-school students. Females are 1% more risky to drop out than males in public schools, while males are 1% more risky to drop out than females in private schools. SMA students are 59% more risky to drop out than SMK students in public schools, while SMK students are 71% more risky to drop out than SMA students in private school.

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Section: Pendahuluanunclassified

Log Linier Binomial Negatif dalam Memodelkan Data Siswa Putus Sekolah Tingkat SMA/SMK

Santi,

Fanya Izmi Hawa,

Bagus Sumargo

2023

JSA

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“…The results of the VIF values are shown in Table 3 below: Table 3. VIF values Explanatory Variables VIF 𝑋 1 1,032 𝑋 2 1,232 𝑋 3 1,398 𝑋 4 1,439 𝑋 5 1,233 𝑋 6 1,061…”

Section: Multicollinearity Testmentioning

confidence: 99%

“…The assumption must be met in the Poisson Regression is the equidispersion condition (variance value is the same as the average value). However, events in the field state that there is often a violation of assumptions in the Poisson Regression, namely the overdispersion condition (variance value is greater than the mean value) [2] caused by many things, namely heterogeneity between observations, the correlation between observations, use of link functions that do not appropriate, improper a systematic component implementation or the excess zero presence [3]. If Poisson regression is still used, the standard error value of the parameter estimates will be too small and can lead to inappropriate conclusions [4].…”

Section: Introductionmentioning

confidence: 99%

Zero Inflated Poisson Regression Analysis in Maternal Death Cases on Java Island

Santi

Ambarwati

Sumargo

2022

Pattimura Int. J. Math.

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The basic regression model used to analyze the count data is the Poisson regression.. However, applying the Poisson regression model is unsuitable for excess zero data because it can cause overdispersion where the variance data is greater than its mean. One of the developments of the Poisson regression model can overcome this condition, Zero Inflated Poisson Regression (ZIP). In the health sector, the death of pregnant women on the Java island is an event that still rarely occurs and forms an excess zero data structure. However, the analysis of cases of maternal mortality using ZIP regression has never been studied in more depth. In this article, the maternal mortality cases in Java were modelled using ZIP regression to specify the variables that had a significant effect. The initial analysis results indicated the occurrence of overdispersion due to excess zero where there are 52% zero values in the data. The ZIP regression applied in this research provides enhancements to the Poisson regression based on the Vuong test. The results showed that the variables that had a significant effect on the maternal death cases in Java in the count model are the percentage of maternal health service coverage and the percentage of coverage of postpartum visit coverage, while in the zero-inflation model, the percentage of deliveries in health facilities and the percentage of obstetric complications treatment

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Count Regression Models for Analyzing Crime Rates in The East Java Province

Cited by 2 publications

References 6 publications

Log Linier Binomial Negatif dalam Memodelkan Data Siswa Putus Sekolah Tingkat SMA/SMK

Log Linier Binomial Negatif dalam Memodelkan Data Siswa Putus Sekolah Tingkat SMA/SMK

Zero Inflated Poisson Regression Analysis in Maternal Death Cases on Java Island

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