Experimental design is often overlooked in the literature of applied and mathematical statistics: statistics is taught and understood as merely a collection of methods for analyzing data. Consequently, experimenters seldom think about optimal design, including prerequisites such as the necessary sample size needed for a precise answer for an experimental question.
Providing a concise introduction to experimental design theory, Optimal Experimental Design with R:
- Introduces the philosophy of experimental design
Provides an easy process for constructing experimental designs and calculating necessary sample size using R programs
Teaches by example using a custom made R program package: OPDOE
Consisting of detailed, data-rich examples, this book introduces experimenters to the philosophy of experimentation, experimental design, and data collection. It gives researchers and statisticians guidance in the construction of optimum experimental designs using R programs, including sample size calculations, hypothesis testing, and confidence estimation. A final chapter of in-depth theoretical details is included for interested mathematical statisticians.
Introduction. Determining the Minimal Size of an Experiment for Given Precision: Sample Size Determination in Completely Randomised Designs. Size of Experiments in Analysis of Variance Models. Sample Size Determination in Model II of Regression Analysis. Sequential Designs. Construction of Optimal Designs: Constructing Balanced Incomplete Block Designs. Constructing Fractional Factorial Designs. Exact Optimal Designs and Sample Sizes in Model I of Regression Analysis. Special Designs. Second Order Designs. Mixture Designs. Theoretical Background.