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.

Keywords

• Multiple linear regression
• Quantile regression
• ANOVA
• Waiting time