### Abstract

Original language | English |
---|---|

Place of Publication | Tilburg |

Publisher | Operations research |

Number of pages | 22 |

Volume | 2007-09 |

Publication status | Published - 2007 |

### Publication series

Name | CentER Discussion Paper |
---|---|

Volume | 2007-09 |

### Fingerprint

### Keywords

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

### Cite this

*Simulation Experiments in Practice: Statistical Design and Regression Analysis*. (CentER Discussion Paper; Vol. 2007-09). Tilburg: Operations research.

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**Simulation Experiments in Practice : Statistical Design and Regression Analysis.** / Kleijnen, J.P.C.

Research output: Working paper › Discussion paper › Other research output

TY - UNPB

T1 - Simulation Experiments in Practice

T2 - Statistical Design and Regression Analysis

AU - Kleijnen, J.P.C.

N1 - Pagination: 22

PY - 2007

Y1 - 2007

N2 - In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic theory assumes a single simulation response that is normally and independently distributed with a constant variance; moreover, the regression (meta)model of the simulation model’s I/O behaviour is assumed to have residuals with zero means. This article addresses the following questions: (i) How realistic are these assumptions, in 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 do hold? (iv) If not, which alternative statistical methods can then be applied?

AB - In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic theory assumes a single simulation response that is normally and independently distributed with a constant variance; moreover, the regression (meta)model of the simulation model’s I/O behaviour is assumed to have residuals with zero means. This article addresses the following questions: (i) How realistic are these assumptions, in 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 do hold? (iv) If not, which alternative statistical methods can then be applied?

KW - metamodels

KW - experimental designs

KW - generalized least squares

KW - multivariate analysis

KW - normality

KW - jackknife

KW - bootstrap

KW - heteroscedasticity

KW - common random numbers

KW - validation

M3 - Discussion paper

VL - 2007-09

T3 - CentER Discussion Paper

BT - Simulation Experiments in Practice

PB - Operations research

CY - Tilburg

ER -