Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Simulation: a statistical perspective
Simulation: a statistical perspective
The 2k-p fractional factorial designs part I
Technometrics
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Theory of Modeling and Simulation
Theory of Modeling and Simulation
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
ANNIVERSARY ARTICLE: Stochastic Simulation Research in Management Science
Management Science
Recent advances in simulation optimization: response surface methodology revisited
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Very large fractional factorial and central composite designs
ACM Transactions on Modeling and Computer Simulation (TOMACS)
White noise assumptions revisited: regression metamodels and experimental designs in practice
Proceedings of the 38th conference on Winter simulation
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Multi-echelon supply chain simulation using metamodel
Proceedings of the 40th Conference on Winter Simulation
Simulation optimization using metamodels
Winter Simulation Conference
Allocation of simulation effort for neural network vs. regression metamodels
Proceedings of the Winter Simulation Conference
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This tutorial explains the basics of linear regression metamodels---especially low-order polynomials--and the corresponding statistical designs---namely, fractional factorial designs of resolution III (Plackett-Burman designs), IV (accounting for interactions), V (estimating individual interactions), and Central Composite Designs (CCDs, for second-order polynomial metamodels). This tutorial assumes 'white noise', which means that the residuals of the fitted linear regression metamodel are normally, independently, and identically distributed with zero mean. This metamodel requires validation. The tutorial gathers statistical results that are scattered throughout the literature on mathematical statistics, and presents these results in a form that is understandable to simulation analysts.