Analysis of Messy Data Volume 1: Designed Experiments, Second Edition

ISBN-13: 9781584883340 / Angielski / Twarda / 2009 / 674 str.

PH.D. George A. Milliken; Dallas E. Johnson; Milliken A. Milliken

Opis

A bestseller for nearly 25 years, Analysis of Messy Data, Volume 1: Designed Experiments helps applied statisticians and researchers analyze the kinds of data sets encountered in the real world. Written by two long-time researchers and professors, this second edition has been fully updated to reflect the many developments that have occurred since the original publication. New to the Second Edition

• Several modern suggestions for multiple comparison procedures
• Additional examples of split-plot designs and repeated measures designs
• The use of SAS-GLM to analyze an effects model
• The use of SAS-MIXED to analyze data in random effects experiments, mixed model experiments, and repeated measures experiments

The book explores various techniques for multiple comparison procedures, random effects models, mixed models, split-plot experiments, and repeated measures designs. The authors implement the techniques using several statistical software packages and emphasize the distinction between design structure and the structure of treatments. They introduce each topic with examples, follow up with a theoretical discussion, and conclude with a case study. Bringing a classic work up to date, this edition will continue to show readers how to effectively analyze real-world, nonstandard data sets.

Recenzje

"…Every chapter has been systematically re-written for greater clarity, and added explanatory material has been inserted throughout. Many new diagrams and redrawn diagrams have been provided; those that show how to lay out the experimental designs are just superb and extraordinarily clear. The reference list has increased … . This revision is highly recommended to those who plan and analyze experiments of the type described."
—International Statistical Review (2009), 77, 2

Spis treści

The Simplest Case: One-Way Treatment Structure in a Completely Randomized Design Structure with Homogeneous Errors. One-Way Treatment Structure in a Completely Randomized Design Structure with Heterogeneous Errors. Simultaneous Inference Procedures and Multiple Comparisons. Basics for Designing Experiments. Multilevel Designs: Split-Plots, Strip-Plots, Repeated Measures, and Combinations. Matrix Form of the Model. Balanced Two-Way Treatment Structures. Case Study: Complete Analyses of Balanced Two-Way Experiments. Using the Means Model to Analyze Balanced Two-Way Treatment Structures with Unequal Subclass Numbers. Using the Effects Model to Analyze Balanced Two-Way Treatment Structures with Unequal Subclass Numbers. Analyzing Large Balanced Two-Way Experiments Having Unequal Subclass Numbers. Case Study: Balanced Two-Way Treatment Structure with Unequal Subclass Numbers. Using the Means Model to Analyze Two-Way Treatment Structures with Missing Treatment Combinations. Using the Effects Model to Analyze Two-Way Treatment Structures with Missing Treatment Combinations. Case Study: Two-Way Treatment Structure with Missing Treatment Combinations. Analyzing Three-Way and Higher-Order Treatment Structures. Case Study: Three-Way Treatment Structure with Many Missing Treatment Combinations. Random Effects Models and Variance Components. Methods for Estimating Variance Components. Methods for Making Inferences about Variance Components. Case Study: Analysis of a Random Effects Model. Analysis of Mixed Models. Case Studies of a Mixed Model. Methods for Analyzing Split-Plot Type Designs. Methods for Analyzing Strip-Plot Type Designs. Methods for Analyzing Repeated Measures Experiments. Analysis of Repeated Measures Experiments When the Ideal Conditions Are Not Satisfied. Case Studies: Complex Examples Having Repeated Measures. Analysis of Crossover Designs. Analysis of Nested Designs. Appendix. Index.

O autorze

George A. Milliken, Dallas E. Johnson