Designed Experiments for Science and Engineering 1st Edition by Michael D Holloway ISBN 1032854413 9781032854410 by Unknown instant download after payment.

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ISBN 10: 1032854413 
ISBN 13: 9781032854410
Author: Michael D Holloway

Designed Experiments for Science and Engineering is a versatile and overarching toolkit that explores various methods of designing experiments for over 20 disciplines in science and engineering. Designed experiments provide a structured approach to hypothesis testing, data analysis, and decision‑making. They allow researchers and engineers to efficiently explore multiple factors, interactions, and their impact on outcomes, ultimately leading to better‑designed processes, products, and systems across a wide range of scientific and engineering disciplines. Each discipline covered in this book includes the key characteristics of the steps in choosing and executing the experimental designs (one factor, fractional factorial, mixture experimentation, factor central composite, 3‑factor + central composite, etc.) and reviews the various statistical tools used as well as the steps in how to utilize each (standard deviation analysis, analysis of variance [ANOVA], relative standard deviation, bias analysis, etc.). This book is essential reading for students and professionals who are involved in research and development within various fields in science and engineering, such as mechanical engineering, environmental science, manufacturing, and aerospace engineering.

Designed Experiments for Science and Engineering 1st Table of contents:

Chapter 1 Introduction
1.1 The First Experiment
1.2 Early Designed Experiments in Engineering
1.3 Early Designed Experiments in Material Science
1.4 Early Designed Experiments in Chemistry
1.5 Fields of Study for Designed Experiments
1.6 Steps in Choosing and Doing the Basic Experimental Designs
1.7 Specific Designs
1.7.1 One Factor
1.7.2 Two Factors
1.7.3 Three or More Factors
Chapter 2 Science Designed Experiments
2.1 Biology
2.1.1 Biology Factorial Experiments
2.1.2 RSM
2.1.3 Fractional Factorial Experiments
2.1.4 CCD
2.1.5 Taguchi Methods in Biology
2.1.6 Mixture Experiments for Biology
2.1.7 Sequential Experimentation for Biology
2.1.8 Robust Parameter Design (RPD)
2.1.9 Randomized Experiments for Biology
2.1.10 Split-Plot and Blocked Experiments
2.2 Chemistry
2.2.1 Factorial Design Experiments in Chemistry
2.2.2 Fractional Factorial Designs
2.2.3 RSM
2.2.4 CCD
2.2.5 Screening Designs
2.2.6 PBDs
2.2.7 Hadamard Designs
2.2.8 Optimization Designs
2.2.9 CCDs
2.2.10 One-Factor-at-a-Time (OFAT) Designs
2.2.11 Nested Designs
2.2.12 Taguchi Method
2.2.13 Sequential Experimentation Design
2.2.14 Forced Failure Method
2.3 Environmental Science
2.3.1 Factorial Experiments
2.3.2 RSM
2.3.3 Fractional Factorial Experiments
2.3.4 CCD
2.3.5 RSM
2.3.6 Taguchi Methods
2.3.7 Mixture Experiments
2.3.8 Sequential Experimentation
2.3.9 RPD
2.3.10 Randomized Experiments
2.3.11 Split-Plot and Blocked Experiments
2.4 Physics
2.4.1 Factorial Experiments
2.4.2 RSM
2.4.3 Fractional Factorial
2.4.4 CCD
2.4.5 Taguchi Methods
2.4.6 Mixture Experiments
2.4.7 Sequential Experimentation
2.4.8 Sequential Design Experiment (SDE)
2.4.9 Randomized Experiments
2.4.10 Split-Plot and Blocked Experiments
2.4.11 BBD
2.4.12 Plackett–Burman
2.4.13 Derringer Design
2.5 Psychology
2.5.1 Factorial Experiments
2.5.2 RSM
2.5.3 Fractional Factorial
2.5.4 CCD
2.5.5 Taguchi Method
2.5.6 Sequential Experimentation
2.5.7 RPD
2.5.8 Randomized Experiments
2.5.9 Split-Plot and Blocked Experiments
2.5.10 Crossover Experiments
2.5.11 BBD
2.5.12 PBD
2.5.13 Derringer Design
Chapter 3 Engineering Designed Experiments
3.1 Aerospace Engineering
3.1.1 Factorial Experiments
3.1.2 Response Surface Methodology (RSM)
3.1.3 Fractional Factorial Experiments
3.1.4 Central Composite Design (CCD)
3.1.5 Taguchi Methods
3.1.6 Mixture Experiments
3.1.7 Sequential Experimentation
3.1.8 Robust Parameter Design (RPD)
3.1.9 Randomized Experiments
3.1.10 Split-Plot and Blocked Experiments
3.2 Chemical Engineering
3.2.1 Factorial Experiments
3.2.2 Response Surface Methodology (RSM)
3.2.3 Fractional Factorial Experiments
3.2.4 Mixture Experiments
3.2.5 Taguchi Method
3.2.6 Central Composite Design (CCD)
3.2.7 Optimization by Stochastic Approximation (SA)
3.2.8 Robust Parameter Design (RPD)
3.2.9 Randomized Experiments
3.2.10 Split-Plot and Blocked Experiments
3.3 Civil Engineering
3.3.1 Factorial Experiments
3.3.2 Response Surface Methodology (RSM)
3.3.3 Fractional Factorial Experiments
3.3.4 Central Composite Design (CCD)
3.3.5 Taguchi Methods
3.3.6 Mixture Experiments
3.3.7 Sequential Experimentation
3.3.8 Robust Parameter Design (RPD)
3.3.9 Randomized Experiments
3.3.10 Split-Plot and Blocked Experiments
3.4 Electrical Engineering
3.4.1 Factorial Experiments
3.4.2 Response Surface Methodology (RSM)
3.4.3 Fractional Factorial Experiments
3.4.4 Central Composite Design (CCD)
3.4.5 Taguchi Methods
3.4.6 Mixture Experiments
3.4.7 Sequential Experimentation
3.4.8 Robust Parameter Design (RPD)
3.4.9 Randomized Experiments
3.4.10 Split-Plot and Blocked Experiments
3.5 Mechanical Engineering
3.5.1 Factorial Experiments
3.5.2 Fractional Factorial Experiments
3.5.3 Central Composite Design (CCD)
3.5.4 Taguchi Methods
3.5.5 Mixture Experiments
3.5.6 Sequential Experimentation
3.5.7 Robust Parameter Design (RPD)
3.5.8 Randomized Experiments
3.5.9 Split-Plot and Blocked Experiments
Chapter 4 Specific Industries
4.1 Agriculture
4.1.1 Factorial Experiments
4.1.2 Response Surface Methodology (RSM)
4.1.3 Fractional Factorial Experiments
4.1.4 Central Composite Design (CCD)
4.1.5 Taguchi Methods
4.1.6 Mixture Experiments
4.1.7 Sequential Experimentation
4.1.8 Robust Parameter Design (RPD)
4.1.9 Randomized Experiments
4.1.10 Split-Plot and Blocked Experiments
4.2 Biotechnology
4.2.1 Factorial Experiments
4.2.2 RSM
4.2.3 Fractional Factorial Experiments
4.2.4 CCD
4.2.5 Taguchi Methods
4.2.6 Mixture Experiments
4.2.7 Sequential Experimentation
4.2.8 RPD
4.2.9 Randomized Experiments
4.2.10 Split-Plot and Blocked Experiments
4.3 Energy and Renewable Resources
4.3.1 Factorial Experiments
4.3.2 RSM
4.3.3 Fractional Factorial Experiments
4.3.4 CCD
4.3.5 Taguchi Methods
4.3.6 Mixture Experiments
4.3.7 Sequential Experimentation
4.3.8 RPD
4.3.9 Randomized Experiments
4.3.10 Split-Plot and Blocked Experiments
4.4 Food Science
4.4.1 Factorial Experiments
4.4.2 RSM
4.4.3 Fractional Factorial Experiments
4.4.4 CCD
4.4.5 Taguchi Methods
4.4.6 Mixture Experiments
4.4.7 Sequential Experimentation
4.4.8 RPD
4.4.9 Randomized Experiments
4.4.10 Split-Plot and Blocked Experiments
4.5 Healthcare and Medical Research
4.5.1 Randomized Controlled Trials (RCTs)
4.5.2 Crossover Trials
4.5.3 Cluster Randomized Trials
4.5.4 Factorial Experiments
4.5.5 RSM
4.5.6 Quasi-Experiments
4.5.7 Sequential Experiments
4.5.8 RPD
4.5.9 Randomized Withdrawal Trials
4.5.10 Simulation Experiments
4.6 Information Technology
4.6.1 Factorial Experiments
4.6.2 RSM
4.6.3 Fractional Factorial Experiments
4.6.4 CCD
4.6.5 Taguchi Methods
4.6.6 Mixture Experiments
4.6.7 Sequential Experimentation
4.6.8 RPD
4.6.9 Randomized Experiments
4.6.10 Split-Plot and Blocked Experiments
4.7 Lubricants
4.7.1 Factorial Experiments
4.7.2 RSM
4.7.3 Mixture Designed Experiments
4.7.4 CCD
4.7.5 Taguchi Methods
4.7.6 Sequential Experimentation
4.7.7 RPD
4.7.8 Randomized Experiments
4.7.9 Split-Plot and Blocked Designs
4.7.10 Fractional Factorial Experiments
4.8 Manufacturing
4.8.1 Factorial Experiments
4.8.2 RSM
4.8.3 Fractional Factorial Experiments
4.8.4 CCD
4.8.5 Taguchi Methods
4.8.6 Mixture Experiments
4.8.7 Sequential Experimentation
4.8.8 RPD
4.8.9 Randomized Experiments
4.8.10 Split-Plot and Blocked Experiments
4.9 Materials Science
4.9.1 Factorial Experiments
4.9.2 RSM
4.9.3 Fractional Factorial Experiments
4.9.4 CCD
4.9.5 Taguchi Methods
4.9.6 Mixture Experiments
4.9.7 Sequential Experimentation
4.9.8 RPD
4.9.9 Randomized Experiments
4.9.10 Split-Plot and Blocked Experiments
4.10 Pharmaceuticals
4.10.1 Factorial Experiments
4.10.2 RSM
4.10.3 Fractional Factorial Experiments
4.10.4 CCD
4.10.5 Taguchi Methods
4.10.6 Mixture Experiments
4.10.7 Sequential Experimentation
4.10.8 RPD
4.10.9 Randomized Experiments
4.10.10 Split-Plot and Blocked Experiments
4.11 Social Sciences
4.11.1 RCTs
4.11.2 Field Experiments
4.11.3 Lab Experiments
4.11.4 Quasi-Experiments
4.11.5 Survey Experiments
4.11.6 Natural Experiments
4.11.7 Crossover Experiments
4.11.8 Longitudinal Experiments
4.11.9 Policy Experiments
4.11.10 Online Experiments
4.12 Transportation and Aerospace
4.12.1 Factorial Experiments
4.12.2 RSM
4.12.3 Fractional Factorial Experiments
4.12.4 CCD
4.12.5 Taguchi Methods
4.12.6 Mixture Experiments
4.12.7 Sequential Experimentation
4.12.8 RPD
4.12.9 Randomized Experiments
4.12.10 Split-Plot and Blocked Experiments
Chapter 5 Statistic Tools Used for Analysis of Precision, Accuracy, Repeatability, and Reproducibility
5.1 How to Do the Statistics for Precision
5.1.1 SD
5.1.2 Variance (Var)/ANOVA
5.1.3 RSD
5.1.4 CV
5.1.5 Control Charts
5.2 How to Do the Statistics for Accuracy
5.2.1 Bias Analysis
5.2.2 Calibration Curves
5.2.3 SRMs
5.2.4 Recovery Experiments
5.3 How to Do the Statistics for Repeatability and Reproducibility
5.3.1 Intra-Assay Variation
5.3.2 Control Charts for Replicates
5.3.3 Method Validation
5.3.4 Ring Tests
5.3.5 Regression Analysis
5.3.6 Multivariate Statistics
5.3.7 Grubbs’ Test
5.3.8 GR&R
5.3.9 Youden Plot
5.4 Examples of Statistics in Use
5.4.1 SD Analysis
5.4.2 ANOVA
5.4.3 RSD
5.4.4 Control Charts
5.4.5 Bias Analysis
5.4.6 Calibration Curves
5.4.7 Standard Reference Analysis
5.4.8 Recovery Experiments
5.4.9 Intra-Assay Variation Analysis
5.4.10 Ring Tests
5.4.11 Grubbs’ Test
5.4.12 GR&R
5.4.13 Youden Plots
References and Reviews
Index

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Tags: Michael D Holloway, Experiments, Science