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Regression analyses of the data sets for the analysis of decomposition error in discrete-time open tandem queues.

ABSTRACT: The data sets and regression models presented here are related to the article "Point and interval estimation of decomposition error in discrete-time open tandem queues" [1]. The data sets are the first to analyze the approximation quality of the discrete-time decomposition approach and contain independent and dependent (explanatory) variables for the analysis of decomposition error, which were obtained using discrete-time queueing models and discrete-event simulation. Independent variables are the utilization parameters of the queues, and variability parameters of the service and arrival processes. Dependent variables are decomposition error with respect to the expected value and 95-percentile of the waiting time distribution at the downstream queue. This article presents multiple linear regression and quantile regression to explain the variance of the dependent variables for tandem queues with equal traffic intensity at both queues and for tandem queues with downstream bottlenecks, respectively.

SUBMITTER: Jacobi C

PROVIDER: S-EPMC9679463 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Regression analyses of the data sets for the analysis of decomposition error in discrete-time open tandem queues.

Jacobi Christoph C Furmans Kai K

Data in brief 20220924

The data sets and regression models presented here are related to the article "Point and interval estimation of decomposition error in discrete-time open tandem queues" [1]. The data sets are the first to analyze the approximation quality of the discrete-time decomposition approach and contain independent and dependent (explanatory) variables for the analysis of decomposition error, which were obtained using discrete-time queueing models and discrete-event simulation. Independent variables are t ...[more]

PMID: 36426088

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Project description:ObjectiveThe study contextualises the spatial heterogeneity and associated drivers of open defecation (OD) in India.DesignThe present study involved a secondary cross-sectional survey data from the fifth round of the National Family Health Survey conducted during 2019-2021 in India. We mapped the spatial heterogeneity of OD practices using LISA clustering techniques and assessed the critical drivers of OD using multivariate regression models. Fairlie decomposition model was used to identify the factors responsible for developing OD hot spots and cold spots.Setting and participantsThe study was conducted in India and included 636 699 sampled households within 36 states and union territories covering 707 districts of India.Primary and secondary outcome measuresThe outcome measure was the prevalence of OD.ResultsThe prevalence of OD was almost 20%, with hot spots primarily located in the north-central belts of the country. The rural-urban (26% vs 6%), illiterate-higher educated (32% vs 4%) and poor-rich (52% vs 2%) gaps in OD were very high. The odds of OD were 2.7 and 1.9 times higher in rural areas and households without water supply service on premises compared with their counterparts. The spatial error model identified households with an illiterate head (coefficient=0.50, p=0.001) as the leading spatially linked predictor of OD, followed by the poorest (coefficient=0.31, p=0.001) and the Hindu (coefficient=0.10, p=0.001). The high-high and low-low cluster inequality in OD was 38%, with household wealth quintile (67%) found to be the most significant contributing factor, followed by religion (22.8%) and level of education (6%).ConclusionThe practice of OD is concentrated in the north-central belt of India and is particularly among the poor, illiterate and socially backward groups. Policy measures should be taken to improve sanitation practices, particularly in high-focus districts and among vulnerable groups, by adopting multispectral and multisectoral approaches.

Dataset Information

Regression analyses of the data sets for the analysis of decomposition error in discrete-time open tandem queues.

Publications

Regression analyses of the data sets for the analysis of decomposition error in discrete-time open tandem queues.

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