is the noise that afflict the experimental results. It arises from variation that is uncontrolled and generally unavoidable. It is distributed as a NID (0, s2) and its unbiased estimator is E(MSE).
Published in Chapter:
Experimental Error Measurement in Monte Carlo Simulation
Lucia Cassettari (University of Genoa, Italy), Roberto Mosca (University of Genoa, Italy), and Roberto Revetria (University of Genoa, Italy)
Copyright: © 2010
|Pages: 51
DOI: 10.4018/978-1-60566-774-4.ch006
Abstract
This chapter describes the set up step series, developed by the Genoa Research Group on Production System Simulation at the beginning of the ’80s, as a sequence, through which it is possible at first statistically validate the simulator, then estimate the variables which effectively affect the different target functions, then obtain, through the regression meta-models, the relations linking the independent variables to the dependent ones (target functions) and, finally, proceed to the detection of the optimal functioning conditions. The authors pay great attention to the treatment, the evaluation and control of the Experimental Error, under the form of Mean Square Pure Error (MSPE), a measurement which is always culpably neglected in the traditional experimentation on the simulation models but, that potentially can consistently invalidate with its magnitude the value of the results obtained from the model.