Design and Analysis of Experiments 9th Edition by Douglas C Montgomery ISBN 9781119113478 1119113474

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ISBN 10: 1119113474
ISBN 13: 9781119113478
Author: Douglas C Montgomery

Design and Analysis of Experiments 9th Edition Table of contents:

Chapter 1: 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

Chapter 2: Simple Comparative Experiments

• Introduction

• Basic Statistical Concepts

• Sampling and Sampling Distributions

• Inferences About the Differences in Means, Randomized Designs

  • Hypothesis Testing

  • Confidence Intervals

  • Choice of Sample Size

  • The Case Where σ₁² ≠ σ₂²

  • The Case Where σ₁² and σ₂² Are Known

  • Comparing a Single Mean to a Specified Value

  • Summary

• Inferences About the Differences in Means, Paired Comparison Designs

  • The Paired Comparison Problem

  • Advantages of the Paired Comparison Design

• Inferences About the Variances of Normal Distributions

• Problems

Chapter 3: Experiments with a Single Factor: The Analysis of Variance

• An Example

• The Analysis of the Variance

• Analysis of the Fixed Effects Model

  • Decomposition of the Total Sum of Squares

  • Statistical Analysis

  • Estimation of the Model Parameters

  • Unbalanced Data

• Model Adequacy Checking

  • The Normality Assumption

  • Plot of Residuals in Time Sequence

  • Plot of Residuals Versus Fitted Values

  • Plots of Residuals Versus Other Variables

• Practical Interpretation of Results

  • A Regression Model

  • Comparisons Among Treatment Means

  • Graphical Comparisons of Means

  • Contrasts

  • Orthogonal Contrasts

  • Scheffé’s Method for Comparing All Contrasts

  • Comparing Pairs of Treatment Means

  • Comparing Treatment Means with a Control

• Sample Computer Output

• Determining Sample Size

  • Operating Characteristic and Power Curves

  • Confidence Interval Estimation Method

• Other Examples of Single-Factor Experiments

  • Chocolate and Cardiovascular Health

  • A Real Economy Application of a Designed Experiment

  • Discovering Dispersion Effects

• The Random Effects Model

  • A Single Random Factor

  • Analysis of Variance for the Random Model

  • Estimating the Model Parameters

• The Regression Approach to the Analysis of Variance

  • Least Squares Estimation of the Model Parameters

  • The General Regression Significance Test

• Nonparametric Methods in the Analysis of Variance

  • The Kruskal-Wallis Test

  • General Comments on the Rank Transformation

• Problems

Chapter 4: Randomized Blocks, Latin Squares, and Related Designs

• The Randomized Complete Block Design

  • Statistical Analysis of the RCBD

  • Model Adequacy Checking

  • Some Other Aspects of the Randomized Complete Block Design

  • Estimating Model Parameters and the General Regression Significance Test

• The Latin Square Design

• The Graeco-Latin Square Design

• Balanced Incomplete Block Designs

  • Statistical Analysis of the BIBD

  • Least Squares Estimation of the Parameters

  • Recovery of Interblock Information in the BIBD

• Problems

Chapter 5: Introduction to Factorial Designs

• Basic Definitions and Principles

• The Advantage of Factorials

• The Two-Factor Factorial Design

  • An Example

  • Statistical Analysis of the Fixed Effects Model

  • Model Adequacy Checking

  • Estimating the Model Parameters

  • Choice of Sample Size

  • The Assumption of No Interaction in a Two-Factor Model

  • One Observation per Cell

• The General Factorial Design

• Fitting Response Curves and Surfaces

• Blocking in a Factorial Design

• Problems

Chapter 6: The 2ᵏ Factorial Design

• Introduction

• The 2² Design

• The 2³ Design

• The General 2ᵏ Design

• A Single Replicate of the 2ᵏ Design

• Additional Examples of Unreplicated 2ᵏ Designs

• 2ᵏ Designs are Optimal Designs

• The Addition of Center Points to the 2ᵏ Design

• Why We Work with Coded Design Variables

• Problems

Chapter 7: Blocking and Confounding in the 2ᵏ Factorial Design

• Introduction

• Blocking a Replicated 2ᵏ Factorial Design

• Confounding in the 2ᵏ Factorial Design

• Confounding the 2ᵏ Factorial Design in Two Blocks

• Another Illustration of Why Blocking Is Important

• Confounding the 2ᵏ Factorial Design in Four Blocks

• Confounding the 2ᵏ Factorial Design in 2ᵖ Blocks

• Partial Confounding

• Problems

Chapter 8: Two-Level Fractional Factorial Designs

• Introduction

• The One-Half Fraction of the 2ᵏ Design

  • Definitions and Basic Principles

  • Design Resolution

  • Construction and Analysis of the One-Half Fraction

• The One-Quarter Fraction of the 2ᵏ Design

• The General 2ᵏ⁻ᵖ Fractional Factorial Design

  • Choosing a Design

  • Analysis of 2ᵏ⁻ᵖ Fractional Factorials

  • Blocking Fractional Factorials

• Alias Structures in Fractional Factorials and Other Designs

• Resolution III Designs

  • Constructing Resolution III Designs

  • Fold Over of Resolution III Fractions to Separate Aliased Effects

  • Plackett-Burman Designs

• Resolution IV and V Designs

  • Resolution IV Designs

  • Sequential Experimentation with Resolution IV Designs

  • Resolution V Designs

• Supersaturated Designs

• Summary

• Problems

Chapter 9: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs

• The 3ᵏ Factorial Design

  • Notation and Motivation for the 3ᵏ Design

  • The 3² Design

  • The 3³ Design

  • The General 3ᵏ Design

• Confounding in the 3ᵏ Factorial Design

  • The 3ᵏ Factorial Design in Three Blocks

  • The 3ᵏ Factorial Design in Nine Blocks

  • The 3ᵏ Factorial Design in 3ᵖ Blocks

• Fractional Replication of the 3ᵏ Factorial Design

  • The One-Third Fraction of the 3ᵏ Factorial Design

  • Other 3ᵏ⁻ᵖ Fractional Factorial Designs

• Factorials with Mixed Levels

  • Factors at Two and Three Levels

  • Factors at Two and Four Levels

• Nonregular Fractional Factorial Designs

  • Nonregular Fractional Factorial Designs for 6, 7, and 8 Factors in 16 Runs

  • Nonregular Fractional Factorial Designs for 9 Through 14 Factors in 16 Runs

  • Analysis of Nonregular Fractional Factorial Designs

• Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool

  • Design Optimality Criterion

  • Examples of Optimal Designs

  • Extensions of the Optimal Design Approach

• Problems

Chapter 11: Response Surface Methods and Designs

• Introduction to Response Surface Methodology

• The Method of Steepest Ascent

• Analysis of a Second-Order Response Surface

  • Location of the Stationary Point

  • Characterizing the Response Surface

  • Ridge Systems

  • Multiple Responses

• Experimental Designs for Fitting Response Surfaces

  • Designs for Fitting the First-Order Model

  • Designs for Fitting the Second-Order Model

  • Blocking in Response Surface Designs

  • Optimal Designs for Response Surfaces

• Experiments with Computer Models

• Mixture Experiments

• Evolutionary Operation

• Problems

Chapter 13: Experiments with Random Factors

• Random Effects Models

• The Two-Factor Factorial with Random Factors

• The Two-Factor Mixed Model

• Rules for Expected Mean Squares

• Approximate F-Tests

• Some Additional Topics on Estimation of Variance Components

  • Approximate Confidence Intervals on Variance Components

  • The Modified Large-Sample Method

• Problems

Chapter 14: Nested and Split-Plot Designs

• The Two-Stage Nested Design

  • Statistical Analysis

  • Diagnostic Checking

  • Variance Components

  • Staggered Nested Designs

• The General m-Stage Nested Design

• Designs with Both Nested and Factorial Factors

• The Split-Plot Design

• Other Variations of the Split-Plot Design

  • Split-Plot Designs with More Than Two Factors

  • The Split-Split-Plot Design

  • The Strip-Split-Plot Design

• Problems

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