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Introduction

  • Peter Goos
Part of the Lecture Notes in Statistics book series (LNS, volume 164)

Abstract

Experiments provide an efficient way of learning as long as they are properly designed and analyzed. Because all experimental observations are subject to random error, an efficient design and analysis of experiments requires statistical methods. In this book, we will concentrate on the statistical design of experiments, rather than on their analysis. In the last couple of decades, experimental design has become increasingly popular in quality engineering, but, as will be illustrated in this book, it is used in nearly any field of study: medicine, agriculture, chemistry, etc. As a result, most of the literature is scattered and terminology and methods are very often specific to the area of application.

Keywords

Central Composite Design Design Point Information Matrix Fractional Factorial Design Design Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Peter Goos
    • 1
  1. 1.Department of Applied EconomicsKatholieke Universiteit LeuvenLeuvenBelgium

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