Buch, Englisch, 628 Seiten, Format (B × H): 161 mm x 238 mm, Gewicht: 1032 g
Buch, Englisch, 628 Seiten, Format (B × H): 161 mm x 238 mm, Gewicht: 1032 g
Reihe: Chapman & Hall/CRC Texts in Statistical Science
ISBN: 978-1-4398-6813-3
Verlag: CRC Press
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.
Zielgruppe
Advanced undergraduate and first-year graduate students in statistics; statisticians, biostatisticians, and industrial engineers.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Introduction. 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. Bibliography. Index.
