George A. Milliken – författare

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.

Mathematical modelling should be regarded as just another tool by microbial ecologists. Unfortunately, they seem to shun mathematical modelling approaches to biological systems. This text, a collaboration between mathematicians and microbial ecologists, attempts to change the state of affairs. It presents approximately a dozen specific applications of mathematical modelling in microbial ecology. Among the areas touched upon are biofilms, biodegradation, bioreactors, the microbes of the gut and rumen, soil systems and nitrogen transformation, and estuarine and benthic systems.

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.

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.

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.

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 proceduresAdditional examples of split-plot designs and repeated measures designsThe use of SAS-GLM to analyze an effects modelThe use of SAS-MIXED to analyze data in random effects experiments, mixed model experiments, and repeated measures experimentsThe 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.