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Design and Analysis of Experiments with SAS

Design and Analysis of Experiments with SAS

Hardback Chapman & Hall/CRC Texts in Statistical Science

By (author) John Lawson, Series edited by Jim Zidek, Series edited by Bradley P. Carlin, Series edited by Martin Abba Tanner, Series edited by Julian J. Faraway

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  • Publisher: Chapman & Hall/CRC
  • Format: Hardback | 596 pages
  • Dimensions: 157mm x 236mm x 36mm | 907g
  • Publication date: 11 May 2010
  • Publication City/Country: Boca Raton, FL
  • ISBN 10: 1420060600
  • ISBN 13: 9781420060607
  • Edition statement: New.
  • Illustrations note: 169 black & white illustrations, 141 black & white tables
  • Sales rank: 1,731,317

Product description

A culmination of the author's many years of consulting and teaching, Design and Analysis of Experiments with SAS provides practical guidance on the computer analysis of experimental data. It connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results. Drawing on a variety of application areas, from pharmaceuticals to machinery, the book presents numerous examples of experiments and exercises that enable students to perform their own experiments. Harnessing the capabilities of SAS 9.2, it includes examples of SAS data step programming and IML, along with procedures from SAS Stat, SAS QC, and SAS OR. The text also shows how to display experimental results graphically using SAS ODS graphics. The author emphasizes how the sample size, the assignment of experimental units to combinations of treatment factor levels (error control), and the selection of treatment factor combinations (treatment design) affect the resulting variance and bias of estimates as well as the validity of conclusions. This textbook covers both classical ideas in experimental design and the latest research topics. It clearly discusses the objectives of a research project that lead to an appropriate design choice, the practical aspects of creating a design and performing experiments, and the interpretation of the results of computer data analysis. SAS code and ancillaries are available at http://lawson.mooo.com

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Author information

John Lawson is a professor in the Department of Statistics at Brigham Young University.

Review quote

This book deserves to be seriously considered as an external reference for a beginning, one semester course in experimental design. It is a handy, reference showing how to get things done quickly using SAS. ... -Francis Giesbrecht, Biometrics, December 2011 The scope of the material coverage is one of the strengths of this book. The author brings a wealth of industrial, consulting, and teaching experience to the book, and adds his great writing style, which keeps the reader paying attention throughout the book. ... one of the biggest strengths of the book is the breadth of coverage of topics and examples relevant to different disciplines. ... The level of detail with respect to SAS codes in this text is excellent. ... I would say that this book fills a gap in guiding the design and analyses of experiments using SAS, and would be well suited as a text for students in applied disciplines who had some linear algebra and statistics background. This book is also an outstanding reference for design and analysis of experiments using SAS. In this book I can find SAS codes for virtually all problems I had to solve in applications, and I will be reaching for this book time and time again in the future. -Alla Sikorskii, The American Statistician, August 2011 The design and analysis of experiments is a fundamental part of statistics, and this book gives a comprehensive treatment of this broad topic. ... this book focuses on linking concepts to practice. ... The examples are taken from a range of areas, including pharmaceutical science and industrial manufacturing. ... A companion website includes SAS code and data sets. The book covers the basics that you would expect ... . A wide variety of other topics are also covered, including split-plot designs, mixture experiments and robust-parameter design. Of particular interest to medical statisticians may be the chapter on crossover and repeated measurement studies ... The inclusion of so many exercises makes this book ideal for teaching. ... I think this book achieves its objectives. It is a comprehensive text on an important subject and it is sure to make designing and analysing experiments in SAS more straightforward. The inclusion of advanced topics and modern methods is a particular benefit in this regard. -David Woods, Statistics in Medicine, 2011 The exposition throughout is first rate. The presentation and organization, the coverage of the topics, and the discussions of the examples are all excellent. If you are an SAS user needing help with experimental design, you will certainly profit from this text. -International Statistical Review (2011), 79, 1 ... The book's strongest point is its wealth of practical examples from a wide range of fields, such as agriculture, industrial production, psychology, pharmacology etc. ... the examples are very helpful for grasping the ideas behind applied experimentation. ... a very useful addition to the library of anyone with an already strong understanding of linear models, some familiarity with SAS, and interest or experience in applied experimentation. ... [also] useful for statistically skilled readers who want to use software other than SAS for design and analysis of experiments. Applied experimenters without a strong statistical background or at least interest will benefit from individual examples... -Journal of Statistical Software, December 2010, Volume 37

Table of contents

Introduction Statistics and Data Collection Beginnings of Statistically Planned Experiments Definitions and Preliminaries Purposes of Experimental Design Types of Experimental Designs Planning Experiments Performing the Experiments Use of SAS Software Completely Randomized Designs with One Factor Introduction Replication and Randomization A Historical Example Linear Model for Completely Randomized Design (CRD) Verifying Assumptions of the Linear Model Analysis Strategies When Assumptions Are Violated Determining the Number of Replicates Comparison of Treatments after the F-Test Factorial Designs Introduction Classical One at a Time versus Factorial Plans Interpreting Interactions Creating a Two-Factor Factorial Plan in SAS Analysis of a Two-Factor Factorial in SAS Factorial Designs with Multiple Factors-Completely Randomized Factorial Design (CRFD) Two-Level Factorials Verifying Assumptions of the Model Randomized Block Designs Introduction Creating a Randomized Complete Block (RCB) Design in SAS Model for RCB An Example of a RCB Determining the Number of Blocks Factorial Designs in Blocks Generalized Complete Block Design Two Block Factors Latin Square Design (LSD) Designs to Study Variances Introduction Random Sampling Experiments (RSE) One-Factor Sampling Designs Estimating Variance Components Two-Factor Sampling Designs-Factorial RSE Nested SE Staggered Nested SE Designs with Fixed and Random Factors Graphical Methods to Check Model Assumptions Fractional Factorial Designs Introduction to Completely Randomized Fractional Factorial (CRFF) Half Fractions of 2k Designs Quarter and Higher Fractions of 2k Designs Criteria for Choosing Generators for 2k-p Designs Augmenting Fractional Factorials Plackett-Burman (PB) Screening Designs Mixed-Level Fractional Factorials Orthogonal Array (OA) Incomplete and Confounded Block Designs Introduction Balanced Incomplete Block (BIB) Designs Analysis of Incomplete Block Designs Partially Balanced Incomplete Block (PBIB) Designs-Balanced Treatment Incomplete Block (BTIB) Youden Square Designs (YSD) Confounded 2k and 2k-p Designs-Completely Confounded Blocked Factorial (CCBF) and Completely Confounded Blocked Fractional Factorial (CCBFF) Confounding 3 Level and p Level Factorial Designs Blocking Mixed Level Factorials and OAs Partial CBF Split-Plot Designs Introduction Split-Plot Experiments with CRD in Whole Plots (CRSP) RCB in Whole Plots (RBSP) Analysis Unreplicated 2k Split-Plot Designs 2k-p Fractional Factorials in Split Plots (FFSP) Sample Size and Power Issues for Split-Plot Designs Crossover and Repeated Measures Designs Introduction Crossover Designs (COD) Simple AB, BA Crossover Designs for Two Treatments Crossover Designs for Multiple Treatments Repeated Measures Designs Univariate Analysis of Repeated Measures Design Response Surface Designs Introduction Fundamentals of Response Surface Methodology Standard Designs for Second-Order Models-Completely Randomized Response Surface (CRRS) Designs Creating Standard Designs in SAS Non-Standard Response Surface Designs Fitting the Response Surface Model with SAS Determining Optimum Operating Conditions Response Surface Designs in Blocks (BRS) Response Surface Designs in Split-Plots (RSSP) Mixture Experiments Introduction Models and Designs for Mixture Experiments Creating Mixture Designs in SAS Analysis of Mixture Experiment Constrained Mixture Experiments Blocking Mixture Experiments Mixture Experiments with Process Variables Mixture Experiments in Split Plot Arrangements Robust Parameter Design Experiments Introduction Noise Sources of Functional Variation Product Array Parameter Design Experiments Analysis of Product Array Experiments Single Array Parameter Design Experiments Joint Modeling of Mean and Dispersion Effects Experimental Strategies for Increasing Knowledge Introduction Sequential Experimentation One-Step Screening and Optimization Evolutionary Operation Concluding Remarks Bibliography Index A Review and Exercises appear at the end of each chapter.