Description
Design of Experiments
An Introduction Based on Linear Models
Chapman & Hall/CRC Texts in Statistical Science Series
Author: Morris Max
Language: EnglishSubjects for Design of Experiments:
Keywords
ANOVA Decomposition; Split Plot ANOVA; experimental design; Regular Fractional Factorial Design; data analysis; Fractional Factorial Design; statistical analysis; Latin Square; linear models; Half Normal Plot; regression models; Resolution Iii; completely randomized designs; Estimable Functions; randomized complete blocks designs; Split Plot Designs; Latin squares; Plackett Burman Designs; orthogonally blocked designs; Fractional Factorial; balanced incomplete block designs; BIBD; random block effects; Fractional Factorial Plan; split-plot designs; Data Set; two-level factorial experiments; Unreplicated Design; factorial group screening experiments; Center Point Runs; optimal design; Resolution II; F2 F2 F2; Regression Experiments; Resolution Iv; Full Factorial Plan; Distinct Design Points; Noncentrality Parameter; Generalized Inverse; Polynomial Models
Publication date: 05-2017
355 p. · 15.6x23.4 cm · Paperback
Publication date: 08-2010
354 p. · 15.6x23.4 cm · Hardback
Description
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Offering deep insight into the connections between design choice and the resulting statistical analysis, Design of Experiments: An Introduction Based on Linear Models explores how experiments are designed using the language of linear statistical models. The book presents an organized framework for understanding the statistical aspects of experimental design as a whole within the structure provided by general linear models, rather than as a collection of seemingly unrelated solutions to unique problems.
The core material can be found in the first thirteen chapters. These chapters cover a review of linear statistical models, completely randomized designs, randomized complete blocks designs, Latin squares, analysis of data from orthogonally blocked designs, balanced incomplete block designs, random block effects, split-plot designs, and two-level factorial experiments. The remainder of the text discusses factorial group screening experiments, regression model design, and an introduction to optimal design. To emphasize the practical value of design, most chapters contain a short example of a real-world experiment. Details of the calculations performed using R, along with an overview of the R commands, are provided in an appendix.
This text enables students to fully appreciate the fundamental concepts and techniques of experimental design as well as the real-world value of design. It gives them a profound understanding of how design selection affects the information obtained in an experiment.
Introduction. Linear Statistical Models. Completely Randomized Designs. Randomized Complete Blocks and Related Designs. Latin Squares and Related Designs. Some Data Analysis for CRDs and Orthogonally Blocked Designs. Balanced Incomplete Block Designs. Random Block Effects. Factorial Treatment Structure. Split-Plot Designs. Two-Level Factorial Experiments: Basics. Two-Level Factorial Experiments: Blocking. Two-Level Factorial Experiments: Fractional Factorials. Factorial Group Screening Experiments. Regression Experiments: First-Order Polynomial Models. Regression Experiments: Second-Order Polynomial Models. Introduction to Optimal Design. Appendices. References. Index.
Max D. Morris is a professor in the Department of Statistics and the Department of Industrial and Manufacturing Systems Engineering at Iowa State University. A fellow of the American Statistical Association, Dr. Morris is a recipient of the National Institute of Statistical Sciences Sacks Award for Cross-Disciplinary Research and the American Society for Quality Wilcoxon Prize.