Analysis of Messy Data Volume 1
Analysis of Messy Data Volume 1

Author: George A. Milliken

Publisher: CRC Press

Published: 2009-03-02

Total Pages: 690

ISBN-13: 1420010158

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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 t

Analysis of Messy Data
Analysis of Messy Data

Author: George A. Milliken

Publisher: CRC Press

Published: 1993-05-15

Total Pages: 498

ISBN-13: 9780412990816

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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.

Analysis of Messy Data, Volume III
Analysis of Messy Data, Volume III

Author: George A. Milliken

Publisher: CRC Press

Published: 2001-08-29

Total Pages: 625

ISBN-13: 1420036181

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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

Analysis of Messy Data, Volume II
Analysis of Messy Data, Volume II

Author: George A. Milliken

Publisher: CRC Press

Published: 2017-01-06

Total Pages: 216

ISBN-13: 1351697129

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Researchers often do not analyze nonreplicated experiments statistically because they are unfamiliar with existing statistical methods that may be applicable. Analysis of Messy Data, Volume II details the statistical methods appropriate for nonreplicated experiments and explores ways to use statistical software to make the required computations feasible.