Applying the Tobit Quantile Regression Model to Improve the Level of Education in Secondary Schools

Hassanien Adel  Salih; Haitham Hassoon  Majid; Maitham Nori  Muhsen

Hassanien Adel Salih Lecturer, General Directorate of Education in Najaf Governorate Najaf, Iraq
Haitham Hassoon Majid Lecturer, General Directorate of Education in Najaf Governorate Najaf, Iraq
Maitham Nori Muhsen Assistant Lecturer, General Directorate of Education in Najaf Governorate Najaf, Iraq

Keywords: Lasso technique, Tobit Quantile Regression model, Hierarchical Bayesian model, Gibbs Sampler algorithm

Abstract

This study aimed to raise secondary school education levels in the Iraqi governorate of Najaf by utilizing the Tobit Quantile Regression model. The shortcomings of response variables are frequently overlooked by traditional regression models, which calls for the use of sophisticated techniques like the Bayesian hierarchical model in conjunction with the Lasso methodology. By identifying important variables influencing schooling, this study fills in the gaps in conventional estimation techniques. Data on characteristics including study hours and parental education were provided by a random sample of one hundred pupils. To improve parameter estimate accuracy, the modified Lasso approach with Gibbs Sampler was applied using the R program. The results highlight the important influence of family and economic circumstances on student achievement and point to the need for focused interventions to improve educational outcomes.

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