Experimental Design for Practitioners
Book
Paperback/Softback
400 pages
978-0-534-38552-1 (ISBN)
Description
EXPERIMENTAL DESIGN FOR PRACTITIONERS covers the subject of design of experiments, especially as applied in engineering and the sciences. Its goal is to provide a practical introduction to the subject, integrating the power of JMP IN to assist the learning process. The coverage is broad and the emphasis is on understanding concepts while using JMP IN to design and analyze experiments, rather than on a theoretical development of the subject.
Reviews / Votes
1. INTRODUCTION TO DESIGNED EXPERIMENTS. Identifying Causes of Variation. Screening Design Example. Basic Terminology and Concepts. 2^2 Design Example. Response Surface Design Example. Why Designed Experiments Are Needed. Some Common Types of Designs. Considerations in Designing an Experiment. 2. GRAPHICAL TOOLS. Types of Data. Graphical Analysis for a Single Variable. Basic Summary Statistics. Boxplots. The Empirical Rule. Graphical Analysis for Paired Data. 3. REVIEW OF UNDERLYING CONCEPTS. Statistical Models. The Normal Distribution. The Standard Normal Distribution. Assessing Normality. Rule of Averages. Confidence Intervals. Hypothesis Tests. Power of a Test. 4. ANALYZING COMPLETELY RANDOMIZED ONE-FACTOR DESIGNS. Analysis of Variance Model. Analysis of Variance Motivation. ANOVA Test - Detecting a Signal in the Presence of Noise. Power of ANOVA Test. Confidence Intervals for Treatment Means. Prediction Intervals. Design Issues for a One-Factor Study. 5. CHECKING MODEL ASSUMPTIONS. Robustness of ANOVA Inferences. Use of Residuals in Testing Assumptions. Remedies for Violations of Assumptions. Implications for Design. 6. TWO-WAY FACTORIAL DESIGNS. Two-way Design Model. Interaction. Test for Main Effects and Interaction. Power of Test. Confidence Intervals for Treatment Means. Prediction Intervals. Response Surface. Design Issues for Two-Factor Studies. 7. TWO-LEVEL FULL FACTORIAL DESIGNS. 2^2 Designs. 2^3 Designs and Three-way Interaction. 2^k Designs. Design Issues for k Factor Studies. 8. TWO-LEVEL FRACTIONAL FACTORIAL DESIGNS. 2^k-r Designs. Analysis of Fractional Factorial Designs Using Model-Based Error. Analysis of Fractional Factorial Designs Without Using Model-Based Error. Sequential Use of Fractions to Separate Effects. Design Issues for 2^k-r Designs. 9. 2^k AND 2^k-r DESIGNS WITH BLOCKING. Blocking a Replicated Design. Blocking an Unreplicated 2^k Design. Blocking 2^k-r Designs. Partial Confounding. Use of Blocking in Designing Experiments. 10. ANALYSIS OF COVARIANCE. Examples with One and Two Covariates. Analysis Using JMP. Assumptions. Use of Covariates in Designing Experiments. 11. FIXED, RANDOM, AND MIXED MODELS. Random Effects and Variance Components. Estimation of and Testing for Variance Components. Mixed Models. Case Study: Repeatability and Reproducibility Study. Design Issues. 12. HIERARCHICAL DESIGNS. Nested Versus Crossed Factors. Experiments with Crossed and Nested Factors. Design Issues. 13. SPLIT-PLOT DESIGNS. Split-Plot Design. Split-Split-Plot Design. Design Issues. 14. RESPONSE SURFACE DESIGNS. Types of Designs. Response Surface Methods. 15. MIXTURE DESIGNS. 16. DESIGN CRITERIA AND ISSUES. Goal of Experimentation. Ability to Estimate Effects of Interest. Setting Levels for the Factors. Neutralizing Nuisance Variation. Estimate of Experimental Error. Randomization. Power. Sequential Strategy. Choice of Factor Levels. 17. TOOLS TO HELP PLAN EXPERIMENTS. FMEA and Its Role in Designing Experiments. Cause-and Effect Matrices. Designing and Successful Experiment. 18. CASE STUDIES . 19. SIX SIGMA. What is It? Role of Design of Experiments.More details
Language
English
Place of publication
CA
United States
Publishing group
Cengage Learning, Inc
Target group
Professional and scholarly
Dimensions
Height: 246 mm
Width: 189 mm
Thickness: 20 mm
ISBN-13
978-0-534-38552-1 (9780534385521)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Content
1. INTRODUCTION TO DESIGNED EXPERIMENTS. Identifying Causes of Variation. Screening Design Example. Basic Terminology and Concepts. 2^2 Design Example. Response Surface Design Example. Why Designed Experiments Are Needed. Some Common Types of Designs. Considerations in Designing an Experiment. 2. GRAPHICAL TOOLS. Types of Data. Graphical Analysis for a Single Variable. Basic Summary Statistics. Boxplots. The Empirical Rule. Graphical Analysis for Paired Data. 3. REVIEW OF UNDERLYING CONCEPTS. Statistical Models. The Normal Distribution. The Standard Normal Distribution. Assessing Normality. Rule of Averages. Confidence Intervals. Hypothesis Tests. Power of a Test. 4. ANALYZING COMPLETELY RANDOMIZED ONE-FACTOR DESIGNS. Analysis of Variance Model. Analysis of Variance Motivation. ANOVA Test - Detecting a Signal in the Presence of Noise. Power of ANOVA Test. Confidence Intervals for Treatment Means. Prediction Intervals. Design Issues for a One-Factor Study. 5. CHECKING MODEL ASSUMPTIONS. Robustness of ANOVA Inferences. Use of Residuals in Testing Assumptions. Remedies for Violations of Assumptions. Implications for Design. 6. TWO-WAY FACTORIAL DESIGNS. Two-way Design Model. Interaction. Test for Main Effects and Interaction. Power of Test. Confidence Intervals for Treatment Means. Prediction Intervals. Response Surface. Design Issues for Two-Factor Studies. 7. TWO-LEVEL FULL FACTORIAL DESIGNS. 2^2 Designs. 2^3 Designs and Three-way Interaction. 2^k Designs. Design Issues for k Factor Studies. 8. TWO-LEVEL FRACTIONAL FACTORIAL DESIGNS. 2^k-r Designs. Analysis of Fractional Factorial Designs Using Model-Based Error. Analysis of Fractional Factorial Designs Without Using Model-Based Error. Sequential Use of Fractions to Separate Effects. Design Issues for 2^k-r Designs. 9. 2^k AND 2^k-r DESIGNS WITH BLOCKING. Blocking a Replicated Design. Blocking an Unreplicated 2^k Design. Blocking 2^k-r Designs. Partial Confounding. Use of Blocking in Designing Experiments. 10. ANALYSIS OF COVARIANCE. Examples with One and Two Covariates. Analysis Using JMP. Assumptions. Use of Covariates in Designing Experiments. 11. FIXED, RANDOM, AND MIXED MODELS. Random Effects and Variance Components. Estimation of and Testing for Variance Components. Mixed Models. Case Study: Repeatability and Reproducibility Study. Design Issues. 12. HIERARCHICAL DESIGNS. Nested Versus Crossed Factors. Experiments with Crossed and Nested Factors. Design Issues. 13. SPLIT-PLOT DESIGNS. Split-Plot Design. Split-Split-Plot Design. Design Issues. 14. RESPONSE SURFACE DESIGNS. Types of Designs. Response Surface Methods. 15. MIXTURE DESIGNS. 16. DESIGN CRITERIA AND ISSUES. Goal of Experimentation. Ability to Estimate Effects of Interest. Setting Levels for the Factors. Neutralizing Nuisance Variation. Estimate of Experimental Error. Randomization. Power. Sequential Strategy. Choice of Factor Levels. 17. TOOLS TO HELP PLAN EXPERIMENTS. FMEA and Its Role in Designing Experiments. Cause-and Effect Matrices. Designing and Successful Experiment. 18. CASE STUDIES . 19. SIX SIGMA. What is It? Role of Design of Experiments.