<P>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.</P><br/><br/><P></P><br/><br/><P>Providing a concise introduction to experimental design theory, <STRONG>Optimal Experimental Design with R</STRONG>:</P><br/><br/><UL><br/><br/><P><br/><br/><LI>Introduces the philosophy of experimental design <BR>Provides an easy process for constructing experimental designs and calculating necessary sample size using R programs<BR>Teaches by example using a custom made R program package: OPDOE</LI><br/><br/><P></P></UL><br/><br/><P></P><br/><br/><P>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. </P>