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.

Analysis of Messy Data, Volume II: Nonreplicated Experiments

Science & Math

Johnson, Dallas E.

Milliken, George A.

Mathematics

Chapman and Hall/CRC

Mathematical Modeling in Microbial Ecology (Chapman & Hall Microbiology Series)

From the Chapman & Hall Microbiology Series this unique resource offers specific experimental and practical applications of mathematical modeling in microbial ecology. The text presents a variety of systems, ranging from subcellular systems to ecosystems, and shows how to test whether the models provide a good representation of the system. The book also encourages further development and application of modeling to burgeoning problems associated with microbial ecology, such as the pollution and destruction of pesticides and herbicides.

Koch, A.L.

Robinson, Joseph A.

Biological Sciences

Springer

Analysis of Messy Data, Volume II

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 I: Designed Experiments

Experiments, Instruments & Measurement

Koch, A.L. L.

At last! A comprehensive, applications-oriented mixed models guide for data analysis. Discover the latest capabilities available for a wide range of applications featuring the MIXED procedure in SAS/STAT software. This practical guide integrates the theory underlaying the models, the specific forms of the models for various applications, and examples from many different fields of study using appropriate SAS code with interpretation of results. 
Specific models discussed include: simple random-effect only, simple mixed with a single fixed and random effect, split-plot, multilocation, repeated measures, analysis of covariance, random coefficients, and spatial correlation. 
With a background in two-way ANOVA and regression and basic knowledge of linear models and matrix algebra, you will benefit from the discussion of basic to advanced topics in this book. A working knowledge of experimental design is also helpful. 
Supports releases 6.09 and higher of SAS software.

SAS System for Mixed Models

Computers & Technology

Littell, Ramon C.

Stroup, Walter W.

Wolfinger, Russell

Certification

SAS Inst

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. Using this strategy, analysts can use their knowledge of regression analysis and analysis of variance to help attack the problem. The authors describe the strategy for one- and two-way treatment structures with one and multiple covariates in a completely randomized design structure. They present new methods for comparing models and sets of parameters, including beta-hat models. They carefully investigate the effect of blocking, explore mixed models, and present a new methodology for using covariates to analyze data from nonreplicated experiments. Analysis of covariance provides an invaluable set of strategies for analyzing data. With its careful balance of theory and examples, Analysis of Messy Data: Volume 3 provides a unique and outstanding guide to the strategy's techniques, theory, and application.

Analysis of Messy Data, Volume III: Analysis of Covariance

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

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

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