Design of Experiments

Design of Experiments : An Introduction Based on Linear Models

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Offering deep insight into the connections between design choice and the resulting statistical analysis, Design of Experiments: An Introduction Based on Linear Models explores how experiments are designed using the language of linear statistical models. The book presents an organized framework for understanding the statistical aspects of experimental design as a whole within the structure provided by general linear models, rather than as a collection of seemingly unrelated solutions to unique problems. The core material can be found in the first thirteen chapters. These chapters cover a review of linear statistical models, completely randomized designs, randomized complete blocks designs, Latin squares, analysis of data from orthogonally blocked designs, balanced incomplete block designs, random block effects, split-plot designs, and two-level factorial experiments. The remainder of the text discusses factorial group screening experiments, regression model design, and an introduction to optimal design. To emphasize the practical value of design, most chapters contain a short example of a real-world experiment. Details of the calculations performed using R, along with an overview of the R commands, are provided in an appendix. This text enables students to fully appreciate the fundamental concepts and techniques of experimental design as well as the real-world value of design. It gives them a profound understanding of how design selection affects the information obtained in an more

Product details

  • Hardback | 376 pages
  • 162.56 x 236.22 x 22.86mm | 680.39g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 13 black & white illustrations
  • 1584889233
  • 9781584889236
  • 1,850,033

About Max Morris

Max D. Morris is a professor in the Department of Statistics and the Department of Industrial and Manufacturing Systems Engineering at Iowa State University. A fellow of the American Statistical Association, Dr. Morris is a recipient of the National Institute of Statistical Sciences Sacks Award for Cross-Disciplinary Research and the American Society for Quality Wilcoxon more

Review quote

A distinctive feature of this excellent book is that it actually focuses on how to design an experiment. ... In all, an original and very useful book for students and instructors. -Stat Papers (2014) 55:1225-1226 the author has succeeded in striking a balance between the choice of topics and depth in discussion for teaching a course. The book is written with a refreshing style and succeeds in conveying the concepts to a reader. The treatment of the subject matter is thorough and the theory is clearly illustrated along with worked examples. Other books are available on similar topics but this book has the advantage that the chapters start with the classical non-matrix-theory approach to introduce the linear model and then converts it into a matrix theory-based linear model. This helps a reader, particularly a beginner, in clearly understanding the transition from a non-matrix approach to a matrix approach and to apply the results of matrix theory over linear models further. -Shalabh, Journal of the Royal Statistical Society, Series A, 2012 Overall, this is a book that is easy to like, with good definitions of designs, few typographical errors, and consistent, straightforward explications of the models ... I can picture a lot of students using a text aimed at a broad market design course but who need to understand more about what is going on behind the curtain. Morris' text also fills that gap very well. -Gary W. Oehlert, Biometrics, May 2012 It is truly my pleasure to read this book ... after reading this book, I benefitted by gaining insights into the modeling aspect of experimental design, and consequentially it helps me appreciate the idea of statistical efficiency behind each design and understand the tools used in data analysis. ... an excellent reference book that I would recommend to anyone who is serious about learning the nuts and bolts of experimental design and data analysis techniques. -Rong Pan, Journal of Quality Technology, Vol. 43, No. 3, July 2011show more

Table of contents

Introduction Example: rainfall and grassland Basic elements of an experiment Experiments and experiment-like studies Models and data analysis Linear Statistical Models Linear vector spaces Basic linear model The hat matrix, least-squares estimates, and design information matrix The partitioned linear model The reduced normal equations Linear and quadratic forms Estimation and information Hypothesis testing and information Blocking and information Completely Randomized Designs Introduction Models Matrix formulation Influence of design on estimation Influence of design on hypothesis testing Randomized Complete Blocks and Related Designs Introduction A model Matrix formulation Influence of design on estimation Influence of design on hypothesis testing Orthogonality and "Condition E" Latin Squares and Related Designs Introduction Replicated Latin squares A model Matrix formulation Influence of design on quality of inference More general constructions: Graeco-Latin squares Some Data Analysis for CRDs and Orthogonally Blocked Designs Introduction Diagnostics Power transformations Basic inference Multiple comparisons Balanced Incomplete Block Designs Introduction A model Matrix formulation Influence of design on quality of inference More general constructions Random Block Effects Introduction Inter- and intra-block analysis CBDs and augmented CBDs BIBDs Combined estimator Why can information be "recovered"? CBD reprise Factorial Treatment Structure Introduction An overparameterized model An equivalent full-rank model Estimation Partitioning of variability and hypothesis testing Factorial experiments as CRDs, CBDs, LSDs, and BIBDs Model reduction Split-Plot Designs Introduction SPD(R,B) SPD(B,B) More than two experimental factors More than two strata of experimental units Two-Level Factorial Experiments: Basics Introduction Example: bacteria and nuclease Two-level factorial structure Estimation of treatment contrasts Testing factorial effects Additional guidelines for model editing Two-Level Factorial Experiments: Blocking Introduction Complete blocks Balanced incomplete block designs Regular blocks of size 2f-1 Regular blocks of size 2f-2 Regular blocks: general case Two-Level Factorial Experiments: Fractional Factorials Introduction Regular fractional factorial designs Analysis Example: bacteria and bacteriocin Comparison of fractions Blocking regular fractional factorial designs Augmenting regular fractional factorial designs Irregular fractional factorial designs Factorial Group Screening Experiments Introduction Example: semiconductors and simulation Factorial structure of group screening designs Group screening design considerations Case study Regression Experiments: First-Order Polynomial Models Introduction Polynomial models Designs for first-order models Blocking experiments for first-order models Split-plot regression experiments Diagnostics Regression Experiments: Second-Order Polynomial Models Introduction Quadratic polynomial models Designs for second-order models Design scaling and information Orthogonal blocking Split-plot designs Bias due to omitted model terms Introduction to Optimal Design Introduction Optimal design fundamentals Optimality criteria Algorithms Appendices References Index A Conclusion and Exercises appear at the end of each more