Design and Analysis of Experiments Enhanced 10th edition by Douglas Montgomery – Ebook PDF Instant Download/Delivery: 1119492440, 9781119492443
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Product details:
ISBN 10: 1119492440
ISBN 13: 9781119492443
Author: Douglas Montgomery
Design and Analysis of Experiments provides a rigorous introduction to product and process design improvement through quality and performance optimization. Clear demonstration of widely practiced techniques and procedures allows readers to master fundamental concepts, develop design and analysis skills, and use experimental models and results in real-world applications. Detailed coverage of factorial and fractional factorial design, response surface techniques, regression analysis, biochemistry and biotechnology, single factor experiments, and other critical topics offer highly-relevant guidance through the complexities of the field.
Stressing the importance of both conceptual knowledge and practical skills, this text adopts a balanced approach to theory and application. Extensive discussion of modern software tools integrate data from real-world studies, while examples illustrate the efficacy of designed experiments across industry lines, from service and transactional organizations to heavy industry and biotechnology. Broad in scope yet deep in detail, this text is both an essential student resource and an invaluable reference for professionals in engineering, science, manufacturing, statistics, and business management.
Design and Analysis of Experiments Enhanced 10th Table of contents:
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Chapter 1: Introduction to Experimental Design
- Focuses on strategies and basic principles of experimentation, historical background, and guidelines for designing experiments.
- Includes problems, study guides, and readings to support learning.
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Chapter 2: Simple Comparative Experiments
- Discusses basic statistical concepts, sampling methods, and how to make inferences about differences in means using randomized and paired comparison designs.
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Chapter 3: Single-Factor Experiments: The Analysis of Variance (ANOVA)
- Introduces the concept of ANOVA for analyzing experiments involving a single factor.
- Covers both fixed and random effects models and the practical interpretation of ANOVA results.
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Chapter 4: Randomized Blocks, Latin Squares, and Related Designs
- Introduces more advanced experimental designs such as the randomized block design, Latin squares, and Graeco-Latin squares, which help control for variability in experiments.
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Chapter 5: Introduction to Factorial Designs
- Introduces the principles behind factorial designs and their advantages, including the 2-factor factorial design and general factorial designs.
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Chapter 6: The 2k Factorial Design
- Focuses on the 2k factorial design, covering basic to advanced methods, and explaining the structure and analysis involved in factorial experiments.
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Chapter 7: Blocking and Confounding in the 2k Factorial Design
- Explores how blocking and confounding impact the interpretation of experimental results, with methods to manage these issues in factorial designs.
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Chapter 8: Two-Level Fractional Factorial Designs
- Discusses fractional factorial designs, including the one-half and one-quarter fractions, and their importance in reducing the number of experimental runs needed while retaining sufficient information.
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Chapter 9: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
- Expands on topics such as 3k factorial designs, confounding in factorial experiments, and the use of optimal design tools.
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Chapter 10: Fitting Regression Models
- Introduces regression modeling, including linear regression models, estimation techniques, hypothesis testing, and diagnostic tools for evaluating model fit.
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Chapter 11: Response Surface Methods and Designs
- Focuses on response surface methodology (RSM) for optimizing processes, including experimental designs for fitting response surfaces and steepest ascent methods.
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Chapter 12: Robust Parameter Design and Process Robustness Studies
- Covers designs aimed at improving process robustness, including crossed array designs and the response model approach for analysis.
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Chapter 13: Experiments with Random Factors
- Introduces random effects models and covers two-factor factorial designs with random factors, estimation of variance components, and F-tests.
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Chapter 14: Nested and Split-Plot Designs
- Discusses nested designs, split-plot designs, and their use in complex experimental setups where factors cannot be fully randomized.
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Chapter 15: Other Design and Analysis Topics
- Includes topics like non-normal responses, unbalanced data in factorial designs, the analysis of covariance (ANCOVA), and repeated measures.
Appendix and Index:
- Appendix: Likely provides additional material or references for detailed analysis, possibly including statistical tables or software tools.
- Bibliography: A list of references for further reading or research.
- Index: A comprehensive index of terms and concepts covered in the textbook.
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