Description
Taylor & Francis Ltd Design And Analysis Of Experiments With Sas 2010 Edition by John Lawson
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 IntroductionStatistics 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 SoftwareCompletely 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-TestFactorial 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 ModelRandomized 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 RSENested SE Staggered Nested SE Designs with Fixed and Random Factors Graphical Methods to Check Model AssumptionsFractional 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 FactorialsPlackett-Burman (PB) Screening Designs Mixed-Level Fractional Factorials Orthogonal Array (OA)Incomplete and Confounded Block DesignsIntroductionBalanced Incomplete Block (BIB) Designs Analysis of Incomplete Block DesignsPartially 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 CBFSplit-Plot DesignsIntroduction 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 DesignsCrossover and Repeated Measures DesignsIntroductionCrossover Designs (COD)Simple AB, BA Crossover Designs for Two TreatmentsCrossover Designs for Multiple TreatmentsRepeated Measures DesignsUnivariate Analysis of Repeated Measures DesignResponse Surface DesignsIntroductionFundamentals 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 ExperimentsIntroductionModels and Designs for Mixture ExperimentsCreating Mixture Designs in SAS Analysis of Mixture ExperimentConstrained Mixture ExperimentsBlocking Mixture ExperimentsMixture Experiments with Process VariablesMixture Experiments in Split Plot ArrangementsRobust Parameter Design ExperimentsIntroductionNoise Sources of Functional VariationProduct Array Parameter Design ExperimentsAnalysis of Product Array ExperimentsSingle Array Parameter Design ExperimentsJoint Modeling of Mean and Dispersion EffectsExperimental Strategies for Increasing KnowledgeIntroductionSequential ExperimentationOne-Step Screening and OptimizationEvolutionary OperationConcluding RemarksBibliographyIndexA Review and Exercises appear at the end of each chapter.