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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 experiment
• Interpret the results of computer data analysis

The 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.

This text 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. R code is used to create and analyze all the example experiments, with the code examples available on the author’s website.

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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 R 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 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 Model
Randomized 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 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)
Definitive Screening Designs
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)
Row Column Designs 
Confounded 2k and 2k-p Designs
Confounding 3 Level and p Level Factorial Designs 
Blocking Mixed-Level Factorials and OAs 
Partially 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 
Creating Standard Response Surface Designs in R 
Non-Standard Response Surface Designs 
Fitting the Response Surface Model with R
Determining Optimum Operating Conditions 
Blocked Response Surface (BRS) Designs  
Response Surface Split-Plot (RSSP) Designs
Mixture Experiments
Introduction
Models and Designs for Mixture Experiments
Creating Mixture Designs in R 
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
An Example of Sequential Experimentation 
Evolutionary Operation
Concluding Remarks
Appendix: Brief Introduction to R 
Answers to Selected Exercises 
Bibliography
Index
A Review and Exercises appear at the end of each chapter.

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