ABSTRACT
Simulation is an essential tool for generating data to be able to visualize and to test data-processing techniques. This chapter reveals simulation techniques that are useful for methods of exploration and validation. There are two basic concepts in the following. The first is the generation of data to represent what might have been sampled from a distribution. The second is to use that data in a simulation of a process to produce surrogate data that might represent experimental results. The Excel functions are single precision valued, and some people object to the fidelity of the distribution to the uniform ideal. Simulators use models of phenomena (processes, equipment) to indicate how the physical item might respond to influences. Models take inputs and predict responses. Conditional probability can be easily expressed in simulation. The mean, or sigma, or other perturbation generator coefficient, can be related to a desired variable.
