Statistical Analysis of Designed Experiments

A indispensable guide to understanding and designing modern
experiments

The tools and techniques of Design of Experiments (DOE) allow
researchers to successfully collect, analyze, and interpret data
across a wide array of disciplines. Statistical Analysis of
Designed Experiments provides a modern and balanced treatment of
DOE methodology with thorough coverage of the underlying theory and
standard designs of experiments, guiding the reader through
applications to research in various fields such as engineering,
medicine, business, and the social sciences.

The book supplies a foundation for the subject, beginning with
basic concepts of DOE and a review of elementary normal theory
statistical methods. Subsequent chapters present a uniform,
model-based approach to DOE. Each design is presented in a
comprehensive format and is accompanied by a motivating example,
discussion of the applicability of the design, and a model for its
analysis using statistical methods such as graphical plots,
analysis of variance (ANOVA), confidence intervals, and hypothesis
tests.

Numerous theoretical and applied exercises are provided in each
chapter, and answers to selected exercises are included at the end
of the book. An appendix features three case studies that
illustrate the challenges often encountered in real-world
experiments, such as randomization, unbalanced data, and outliers.
Minitab® software is used to perform analyses throughout the
book, and an accompanying FTP site houses additional exercises and
data sets.

With its breadth of real-world examples and accessible treatment
of both theory and applications, Statistical Analysis of Designed
Experiments is a valuable book for experimental design courses at
the upper-undergraduate and graduate levels. It is also an
indispensable reference for practicing statisticians, engineers,
and scientists who would like to further their knowledge of
DOE.

Auteur

Ajit C. Tamhane, PhD, is Professor of Industrial Engineering and Management Sciences at Northwestern University. A Fellow of the American Statistical Society, Institute of Mathematical Statistics, American Association for Advancement of Science and an elected member of the International Statistical Institute, Dr. Tamhane has over forty years of academic and consulting experience in the areas of applied and mathematical statistics. He is the coauthor of Multiple Comparison Procedures and a forthcoming book on Predictive Analytics: Parametric Models for Regression and Classification Using R, also published by Wiley. He is also the coauthor of Statistics and Data Analysis: From Elementary to Intermediate.

Texte du rabat

A indispensable guide to understanding and designing modern experiments

The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. Statistical Analysis of Designed Experiments provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences.

The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphical plots, analysis of variance (ANOVA), confidence intervals, and hypothesis tests.

Numerous theoretical and applied exercises are provided in each chapter, and answers to selected exercises are included at the end of the book. An appendix features three case studies that illustrate the challenges often encountered in real-world experiments, such as randomization, unbalanced data, and outliers. Minitab® software is used to perform analyses throughout the book, and an accompanying FTP site houses additional exercises and data sets.

With its breadth of real-world examples and accessible treatment of both theory and applications, Statistical Analysis of Designed Experiments is a valuable book for experimental design courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing statisticians, engineers, and scientists who would like to further their knowledge of DOE.

Résumé
A indispensable guide to understanding and designing modern experiments The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. Statistical Analysis of Designed Experiments provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences.

The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphical plots, analysis of variance (ANOVA), confidence intervals, and hypothesis tests.

Numerous theoretical and applied exercises are provided in each chapter, and answers to selected exercises are included at the end of the book. An appendix features three case studies that illustrate the challenges often encountered in real-world experiments, such as randomization, unbalanced data, and outliers. Minitab® software is used to perform analyses throughout the book, and an accompanying FTP site houses additional exercises and data sets.

With its breadth of real-world examples and accessible treatment of both theory and applications, Statistical Analysis of Designed Experiments is a valuable book for experimental design courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing statisticians, engineers, and scientists who would like to further their knowledge of DOE.

Contenu

Preface xv

Abbreviations xxi

1 Introduction 1

1.1 Observational Studies and Experiments 1

1.2 Brief Historical Remarks 4

1.3 Basic Terminology and Concepts of Experimentation 5

1.4 Basic Principles of Experimentation 9

1.4.1 How to Minimize Biases and Variability? 9

1.4.2 Sequential Experimentation 14

1.5 Chapter Summary 15

Exercises 16

2 Review of Elementary Statistics 20

2.1 Experiments for a Single Treatment 20

2.1.1 Summary Statistics and Graphical Plots 21

2.1.2 Confidence Intervals and Hypothesis Tests 25

2.1.3 Power and Sample Size Calculation 27

2.2 Experiments for Comparing Two Treatments 28

2.2.1 Independent Samples Design 29

2.2.2 Matched Pairs Design 38

2.3 Linear Regression 41

2.3.1 Simple Linear Regression 42

2.3.2 Multiple Linear Regression 50

2.4 Chapter Summary 62

Exercises 62

3 Single Factor Experiments: Completely Randomized Designs 70

3.1 Summary Statistics and Graphical Displays 71

3.2 Model 73

3.3 Statistical Analysis 75

3.3.1 Estimation 75

3.3.2 Analysis of Variance 76

3.3.3 Confidence Intervals and Hypothesis Tests 78

3.4 Model Diagnostics 79

3.4.1 Checking Homoscedasticity 80

3.4.2 Checking Normality 81

3.4.3 Checking Independence 81

3.4.4 Checking Outliers 81

3.5 Dat…