596 pages, 100 b/w illustrations, tables
One of the first texts to use R to illustrate the construction of experimental designs and analysis of data, Design and Analysis of Experiments with R covers both classical ideas in experimental design and the latest research topics. It clearly discusses the objectives of a research project that lead to an appropriate design choice, the practical aspects of creating a design and performing experiments, and the interpretation of the results of computer data analysis. It also includes many applications from the pharmaceutical, agricultural, industrial chemicals, and machinery industries. A solutions manual is available for qualifying instructors.
"This is an excellent but demanding text. [...] This book should be mandatory reading for anyone teaching a course in the statistical design of experiments. [...] reading this text is likely to influence their course for the better."
– MAA Reviews, March 2015
- Completely Randomized Designs with One Factor
- Factorial Designs
- Randomized Block Designs
- Designs to Study Variances
- Fractional Factorial Designs
- Incomplete and Confounded Block Designs
- Split-Plot Designs
- Crossover and Repeated Measures Designs
- Response Surface Designs
- Mixture Experiments
- Robust Parameter Design Experiments
- Experimental Strategies for Increasing Knowledge
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John Lawson is a professor in the Department of Statistics at Brigham Young University.