Description
Taylor & Francis Ltd Design And Analysis Of Experiments With R 2014 Edition by John Lawson
Design and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the 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 his many years of working in the pharmaceutical, agricultural, industrial chemicals, and machinery industries, the author teaches students how to:Make an appropriate design choice based on the objectives of a research project Create a design and perform an experimentInterpret the results of computer data analysisThe book emphasizes the connection among the experimental units, the way treatments are randomized to experimental units, and the proper error term for data analysis. R code is used to create and analyze all the example experiments. The code examples from the text are available for download on the author's website, enabling students to duplicate all the designs and data analysis.Intended for a one-semester or two-quarter course on experimental design, this text covers classical ideas in experimental design as well as the latest research topics. It gives students practical guidance on using R to analyze experimental data. 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 R 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 R Analysis of a Two-Factor Factorial in R 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 R 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)Definitive Screening DesignsIncomplete and Confounded Block DesignsIntroductionBalanced Incomplete Block (BIB) Designs Analysis of Incomplete Block DesignsPartially Balanced Incomplete Block (PBIB) Designs-Balanced Treatment Incomplete Block (BTIB)Row Column Designs Confounded 2k and 2k-p DesignsConfounding 3 Level and p Level Factorial Designs Blocking Mixed-Level Factorials and OAs Partially 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 Creating Standard Response Surface Designs in R Non-Standard Response Surface Designs Fitting the Response Surface Model with RDetermining Optimum Operating Conditions Blocked Response Surface (BRS) Designs Response Surface Split-Plot (RSSP) DesignsMixture ExperimentsIntroductionModels and Designs for Mixture ExperimentsCreating Mixture Designs in R 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 OptimizationAn Example of Sequential Experimentation Evolutionary OperationConcluding RemarksAppendix: Brief Introduction to R Answers to Selected Exercises BibliographyIndexA Review and Exercises appear at the end of each chapter.