Design and Analysis of Experiments With R
Chapman & Hall/CRC Texts in Statistical Science Series

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Language: Anglais
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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.
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
. reating 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
John Lawson is a professor in the Department of Statistics at Brigham Young University.