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Optimal Experimental Design with R
Contributor(s): Rasch, Dieter (Author), Pilz, Jurgen (Author), Verdooren, L. R. (Author)
ISBN: 1439816972     ISBN-13: 9781439816974
Publisher: CRC Press
OUR PRICE:   $152.00  
Product Type: Hardcover - Other Formats
Published: March 2010
Qty:
Annotation:

Optimal Experimental Design with R introduces experimenters to the philosophy of experimentation and the need for good design and data collection for experiments. It gives experimenters and statisticians guidance on how to construct optimum experimental designs and calculate the sample size needed using R programs. The book contains many detailed real-world examples that show how the R programs should be used. The authors discuss variations of the regression model, including linear and nonlinear regression models. They also provide a final chapter of theoretical details for interested mathematical statisticians.
Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - General
Dewey: 519.570
LCCN: 2011023001
Physical Information: 0.81" H x 6.14" W x 9.21" (1.45 lbs) 345 pages
 
Descriptions, Reviews, Etc.
Publisher Description:

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.