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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.
- Sales Rank: #1152227 in Books
- Published on: 2004-07-26
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 1.40" w x 7.10" l, 3.00 pounds
- Binding: Hardcover
- 674 pages
About the Author
Kansas State University, Manhattan, Kansas, USA
Most helpful customer reviews
2 of 5 people found the following review helpful.
Great Resource
By poorstudent2469
This book has wonderful explanations that are easy to follow and several examples to better understand the material. It's a great resource that I have referenced on several occasions. I highly recommend the text.
0 of 0 people found the following review helpful.
I always feel that I understand these concepts better when they can be placed in a 'real world ...
By Orelia E Dann
Textbook for Experimental Design for Product Development. So far I have only used it as a complimentary resource to learning 2^n factorial designs, but it is well written. Also helps that one of the authors is my professor who explains statistical concepts well for applications and not pure theory. I always feel that I understand these concepts better when they can be placed in a 'real world setting'.
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