Statistical Analysis of Designed Experiments

This book provides a balanced coverage of underlying theory of statistical analysis of designed experiments and its numerous applications. Data sets from real life studies are used throughout, and graphical as well as formal analyses are illustrated using MINITAB software. Applications are drawn from diverse areas including agriculture, biology, engineering, marketing, medicine and psychology. Thus the book should be of interest to students from a wide variety of disciplines with the necessary mathematical and statistical backgrounds. The book should also serve as a good reference source to practitioners.

Ajit C. Tamhane, PhD, is Professor of Industrial Engineering and Management Sciences and Senior Associate Dean of the McCormick School of Engineering and Applied Science at Northwestern University. A Fellow of the American Statistical Society, Dr. Tamhane has over thirty years of academic and consulting experience in the areas of applied and mathematical statistics. He is the coauthor of Multiple Comparison Procedures, also published by Wiley.

Introduction
Observational Studies and Experiments
Brief Historical Remarks
Basic Terminology and Concepts of Experimentation
Basic Principles of Experimentation
Review of Elementary Statistics
Experiments for a Single Treatment
Experiments for Comparing Two Treatments
Linear Regression
Chapter Summary
Exercises
Single Factor Experiments: Completely Randomized Designs
Summary Statistics and Graphical Displays
Model
Statistical Analysis
Model Diagnostics
Data Transformations
Power of the F-test and Sample Size Determination
Quantitative Treatment Factors
One-Way Analysis of Covariance
Chapter Notes
Chapter Summary
Exercises
Single Factor Experiments: Multiple Comparison and Selection Procedures
Basic Concepts of Multiple Comparisons
Pairwise Comparisons
Comparisons with a Control
General Contrasts
Ranking and Selection Procedures
Chapter Summary
Exercises
Randomized Block Designs and Extensions
Randomized Block (RB) Designs
Balanced Incomplete Block (BIB) Designs
Youden Square (YSQ) Designs
Latin Square (LSQ) Designs
Chapter Notes
Chapter Summary
Exercises
General Factorial Experiments
Factorial vs. One-Factor-at-a-Time Experiments
Balanced Two-Way Layouts
Unbalanced Two-Way Layouts
Chapter Notes
Chapter Summary
Exercises
Two-Level Factorial Experiments
Estimation of Main Effects and Interactions
Statistical Analysis
Single Replicate Case
Factorial Designs in Incomplete Blocks: Confounding of Effects
Chapter Notes
Chapter Summary
Exercises
Two-Level Fractional Factorial Experiments
Two-Level Fractional Factorial Experiments
Plackett-Burman Designs
Hadamard Designs
Supersaturated Designs
Orthogonal Arrays
Sequential Assemblies of Fractional Factorials
Chapter Summary
Exercises
Three-Level and Mixed-Level Factorial Designs
Three-Level Full Factorial Designs
Three-Level Fractional Factorial Designs
Mixed-Level Factorial Designs
Chapter Notes
Chapter Summary
Exercises
Experiments for Response Optimization
Response Surface Methodology
Mixture
The Taguchi Method of Quality Improvement
Chapter Summary
Exercises
Random and Mixed Crossed Factors Designs
One-Way Layouts
Two-Way Layouts
Three-Way Layouts
Chapter Notes
Chapter Summary
Exercises
Nested, Crossed-Nested and Split Plot Designs
Two-Stage Nested Designs
Three-Stage Nested Designs
Crossed and Nested Designs
Split Plot Designs
Chapter Notes
Chapter Summary
Exercises
Repeated Measures Designs
Repeated Measures Designs: Univariate Approach
Rep
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Statistical Analysis of Designed Experiments Theory and Applications

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Table of Contents

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