White Noise Assumption Srevisited: Regression Meta Models and Experimental Designs in Practice

Classic linear regression metamodels and their concomitant experimental designs assume a univariate (not multivariate) simulation response and white noise. By definition, white noise is normally (Gaussian), independently (implying no common random numbers), and identically (constant variance) distributed with zero mean (valid metamodel). This advanced tutorial tries to answer the following questions: (i) How realistic are these classic assumptions in simulation practice? (ii) How can these assumptions be tested? (iii) If assumptions are violated, can the simulation's I/O data be transformed such that the analysis becomes correct? (iv) If such transformations cannot be applied, which alternative statistical methods (for example, generalized least squares, bootstrapping, jackknifing) can then be applied?