### Abstract

Classic linear regression models and their concomitant statistical designs assume a univariate response and white noise.By definition, white noise is normally, independently, and identically distributed with zero mean.This survey 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 assumptions hold?(iv) If not, which alternative statistical methods can then be applied?

Original language | English |
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Place of Publication | Tilburg |

Publisher | Operations research |

Number of pages | 21 |

Volume | 2006-50 |

Publication status | Published - 2006 |

### Publication series

Name | CentER Discussion Paper |
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Volume | 2006-50 |

### Keywords

- metamodels
- experimental designs
- generalized least squares
- multivariate analysis
- normality
- jackknife
- bootstrap
- heteroscedasticity
- common random numbers
- validation

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## Cite this

Kleijnen, J. P. C. (2006).

*White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice*. (CentER Discussion Paper; Vol. 2006-50). Operations research.