Design and Analyse Your Experiment Using MINITAB / Edition 1

by Tony Greenfield, Andrew V. Metcalfe

Overview

Progress in engineering and the physical sciences, agriculture and the biological sciences, and to some extent social science, depends on experiments. The design of such experiments is crucial. If they are poorly designed they will be inefficient and may lead to misleading conclusions. Nevertheless, many investigators and researchers in industry and universities are expected to design and analyze their own experiments. Even if investigators do have access to statistical advice, they will be expected to have some basic knowledge of the issues. This book aims to help. Covering all the most commonly used designs of experiments, the methods and the potential pitfalls are described in clear English. The techniques are introduced with case studies of practical significance. The cases are based on real experiments but are described in the context of three fictitious organizations: an engineering company, SeaDragon; a pharmaceuticals and chemicals manufacturer AgroPharm; and the Department of Social Studies at the University of Erewhon. All technical terms are defined and the mathematical development is restricted to that which is needed to use MINITAB.



To note: the text makes reference to the following URL: www.greenfieldresearch.co.uk/doe/data.htm.

However, this URL has since been updated to the following: https://web.archive.org/web/20161117011155/http://www.greenfieldresearch.co.uk/doe/data.htm 

Please use this link to have access to the supplementary material.

Product Details

ISBN-13:	9780470711149
Publisher:	Wiley
Publication date:	06/28/2010
Pages:	312
Product dimensions:	7.43(w) x 9.65(h) x 0.62(d)

About the Author

Tony Greenfield is an Industrial Research Consultant.

Andrew Metcalfe is a Senior Lecturer in Statistics at the University of Adelaide.

Table of Contents

Preface.

Notation.

Website for data sets.

Sources of data.

1 Guide.

2 Descriptive statistics and plotting.

3 Single sample experiments.

4 Comparison proportions.

5 Comparing two treatments.

6 Comparison of several means.

7 Factorial experiments with factors at two levels.

8 Fractional factorial experiments with factors at two levels.

9 Response surfaces.

10 Hill climbing.

11 Robust design.

12 Hierarchical (nested) designs.

13 Two factors at several levels.

14 Crossed and nested factors, and split-plot designs.

15 Mixture designs.

16 Discrete response.

Post-script.

Authors.

Glossary.

References.

Index.