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eBook Analysis of Messy Data, Volume I: Designed Experiments ePub

eBook Analysis of Messy Data, Volume I: Designed Experiments ePub

by George A. Milliken,Dallas E. Johnson

  • ISBN: 0412990814
  • Category: Mathematics
  • Subcategory: Math Science
  • Author: George A. Milliken,Dallas E. Johnson
  • Language: English
  • Publisher: Chapman and Hall/CRC; 1 edition (May 15, 1984)
  • Pages: 490
  • ePub book: 1964 kb
  • Fb2 book: 1147 kb
  • Other: lrf rtf mobi lrf
  • Rating: 4.1
  • Votes: 430

Description

Analysis of covariance is a very useful but often misunderstood methodology for analyzing data where important characteristics of the experimental units are measured but not included as factors in the design.

Analysis of covariance is a very useful but often misunderstood methodology for analyzing data where important characteristics of the experimental units are measured but not included as factors in the design. Analysis of Messy Data, Volume 3: Analysis of Covariance takes the unique approach of treating the analysis of covariance problem by looking at a set of regression models, one for each of the treatments or treatment combinations. Analysis of covariance is a very useful but often misunderstood methodology for analyzing data where important characteristics of the experimental units are measured but not included as factors in the design.

George A. Milliken, Dallas E. Johnson. CRC Press, 15 mei 1993 - 490 pagina's. This classic reference details methods for effectively analyzing non-standard or messy data sets. The authors introduce each topic with examples, follow up with a theoretical discussion, and conclude with a case study.

Use features like bookmarks, note taking and highlighting while reading Analysis of Messy Data Volume 1. .This book has wonderful explanations that are easy to follow and several examples to better understand the material.

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.

Analysis of Messy Data Vol. 1 : Designed Experiments. by George A. Milliken. This classic reference details methods for effectively analyzing non-standard or messy data sets

Analysis of Messy Data Vol.

Finding books BookSee BookSee - Download books for free. George A. Category: Математика, Алгоритмы и структуры данных. 7 Mb. Analysis of Messy Data, Volume III: Analysis of Covariance. Johnson

George A.

This classic reference details methods for effectively analyzing non-standard or messy data sets. The authors introduce each topic with examples, follow up with a theoretical discussion, and conclude with a case study. They emphasize the distinction between design structure and the structure of treatments and focus on using the techniques with several statistical packages, including SAS, BMDP, and SPSS.

Comments

IGOT IGOT
One of my professional students had a PhD in statistics and trained with one of the authors. She introduced me to this set of books. My original paper back copy of this first volume has more tape holding it together than the original binding. That's a mark of extensive use. There are so many situations where the actual data available departs from the expected norms. This is the series (and I recommend the entire series) that helps you deal with it.
This is not a book for beginners. To understand and get full utility, the reader needs to understand not just basic statistics, but at least the intermediate levels. If you just want to point and click on menu buttons in a commercial statistical application, you can get "results" and this series of books is probably not for you. In contrast, if you perform statistical evaluations in the "real world" on a regular basis and want to understand how data anomalies affect the statistical results, and to optimize the validity of analyses on messy data, this series of books is exceptional. I highly recommend the entire series.
Sha Sha
Messy data is data that does fit into the structure to be directly analyzed by the standard methods. In the case of linear models based on designed experiments this can be due to missing data or unbalanced data. The authors do a fabulous job of laying out the various situations that involve methods for handling such problems. By separating the material into three volumes the authors can concentrate on the nitty gritty details as they do here in this volume, the longest of the three.