Design and Analysis of Experiments, 7th Edition

by Douglas C. Montgomery (Arizona State University )

• ISBN13: 9780470128664
• ISBN10: 0470128666
• Edition: 7th
• Format: Hardcover
• Copyright: 2008-07-01
• Publisher: Wiley

Summary

This bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments. The new edition includes more software examples taken from the three most dominant programs in the field: Minitab, JMP, and SAS. Additional material has also been added in several chapters, including new developments in robust design and factorial designs. New examples and exercises are also presented to illustrate the use of designed experiments in service and transactional organizations. Engineers will be able to apply this information to improve the quality and efficiency of working systems.

Author Biography

Douglas C Montgomery Regents Professor of Industrial Engineering and Statistics at Arizona State University, received his B.S., M.S., and Ph.D. degrees from Virginia Polytechnic Institute, all in engineering. From 1969 to 1984, he was a faculty member of the School of Industrial & Systems Engineering at the Georgia Institute of Technology; from 1984 to 1988, he was at University of Washington, where he held the John M. Fluke Distinguished Chair of Manufacturing Engineering, was Professor of Mechanical Engineering, and Director of the Program in Industrial Engineering. He has authored and coauthored many technical papers as well as twelve other books. Dr. Montgomery is a Stewart Medalist of the American Society for Quality, and has also received the Brumbaugh Award, the William G. Hunter Award, and the Shewell Award (twice) from the ASQ.

Table of Contents

Preface

Introduction.

Strategy of Experimentation

Some Typical Applications of Experimental Design

Basic Principles

Guidelines for Designing Experiments

A Brief History of Statistical Design

Summary: Using Statistical Techniques in Experimentation

Problems

Simple Comparative Experiments.

Introduction

Basic Statistical Concepts

Sampling and Sampling Distributions

Inferences About the Differences in Means, Randomized Designs

Inferences About the Differences in Means, Paired Comparison Designs

Inferences About the Variances of Normal Distributions

Problems

Experiments with a Single Factor: The Analysis of Variance.

An Example

The Analysis of Variance

Analysis of the Fixed Effects Model

Model Adequacy Checking

Practical Interpretation of Results

Sample Computer Output

Determining Sample Size

A Real Economy Application of a Designed Experiment

Discovering Dispersion Effects

The Regression Approach to the Analysis of Variance

Nonparametric Methods in the Analysis of Variance

Problems

Randomized Blocks, Latin Squares, and Related Designs.

The Randomized Complete Block Design

The Latin Square Design

The Graeco-Latin Square Design

Balanced Incomplete Block Designs

Problems

Introduction to Factorial Designs.

Basic Definitions and Principles

The Advantage of Factorials

The Two-Factor Factorial Design

The General Factorial Design

Fitting Response Curves and Surfaces

Blocking in a Factorial Design

Problems

The 2^k Factorial Design.

Introduction

The 2² Design

The 2³ Design

The General 2^k Design

A Single Replicate of the 2^k Design

Additional Examples of Unreplicated 2^k Design

2^k Designs are Optimal Designs

The Addition of Center Points to the 2^k Design

Why We Work with Coded Design Variables

Problems

Blocking and Confounding in the 2^k Factorial Design.

Introduction

Blocking a Replicated 2^k Factorial Design

Confounding in the 2^k Factorial Design

Confounding the 2^k Factorial Design in Two Blocks

Another Illustration of Why Blocking Is Important

Confounding the 2^k Factorial Design in Four Blocks

Confounding the 2^k Factorial Design in 2^p Blocks

Partial Confounding

Problems

Two-Level Fractional Factorial Designs.

Introduction

The One-Half Fraction of the 2^k Design

The One-Quarter Fraction of the 2^k Design

The General 2^kûp Fractional Factorial Design

Alias Structures in Fractional Factorials and other Designs

Resolution III Designs

Resolution IV and V Designs

Supersaturated Designs

Summary

Problems

Three-Level and Mixed-Level Factorial and Fractional Factorial Designs.

The 3^k Factorial Design

Confounding in the 3^k Factorial Design

Fractional Replication of the 3^k Factorial Design

Factorials with Mixed Levels

Fitting Regression Models.

Introduction

Linear Regression Models

Estimation of the Parameters in Linear Regression Models

Hypothesis Testing in Multiple Regression

Confidence Intervals in Multiple Regression

Prediction of New Response Observations

Regression Model Diagnostics

Testing for Lack of Fit

Problems

Response Surface Methods and Designs.

The Method of Steepest Ascent

Analysis of a Second-Order Response Surface

Experimental Designs for Fitting Response Surfaces

Experiments with Computer Models

Mixture Experiments

Evolutionary Operation

Problems

Robust Parameter Design and Process Robustness Studies.

Introduction

Crossed Array Designs

Analysis of the Crossed Array Design

Combined Array Design and the Response Model Approach

Choice of Designs

Problems

Experiments with Random Factors.

The Random Effects Model

The Two-Factor Factorial with Random Factors

The Two-Factor Mixed Model

Sample Size Determination with Random Effects

Rules for Expected Mean Squares

Approximate F Tests

Some Additional Topics on Estimation of Variance Components

Problems

Nested and Split-Plot Designs.

The Two-Stage Nested Design

The General m-Stage Nested Design

Designs with Both Nested and Factorial Factors

The Split-Plot Design

Other Variations of the Split-Plot Design

Problems

Other Design and Analysis Topics.

Nonnormal Responses and Transformations

Unbalanced Data in a Factorial Design

The Analysis of Covariance

Repeated Measures

Problems

Appendix

Cumulative Standard Normal Distribution

Percentage Points of the t Distribution

Percentage Points of the X² Distribution

Percentage Points of the F Distribution

Operating Characteristic Curves for the Fixed Effects Model Analysis of Variance

Operating Characteristic Curves for the Random Effects Model Analysis of Variance

Percentage Points of the Studentized Range Statistic

Critical Values for DunnettÆs Test for Comparing Treatments with a Control

Coefficients of Orthogonal Polynomials

Alias Relationships for 2^kûp Fractional Factorial Designs with k <15 and n <64

Bibliography.

Index.

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