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

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

Statisticians and nonstatisticians alike will appreciate this modern and comprehensive new book on multivariate statistical methods that utilizes statistical computing packages throughout. Author Dallas Johnson uses real-life examples and explains the "when to," "why to," and "how to" of numerous multivariate methods, stressing the importance and practical application of each. Technical details are kept to a minimum, making the book accessible to readers.

Applied Multivariate Methods for Data Analysts

Duxbury Press

Applied Regression and ANOVA Using SAS

Applied Regression and ANOVA Using SAS(R) has been written specifically for non-statisticians and applied statisticians who are primarily interested in what their data are revealing. Interpretation of results are key throughout this intermediate-level applied statistics book. The authors introduce each method by discussing its characteristic features, reasons for its use, and its underlying assumptions. They then guide readers in applying each method by suggesting a step-by-step approach while providing annotated SAS programs to implement these steps.Those unfamiliar with SAS software will find this book helpful as SAS programming basics are covered in the first chapter. Subsequent chapters give programming details on a need-to-know basis. Experienced as well as entry-level SAS users will find the book useful in applying linear regression and ANOVA methods, as explanations of SAS statements and options chosen for specific methods are provided.Features: -Statistical concepts presented in words without matrix algebra and calculus -Numerous SAS programs, including examples which require minimum programming effort to produce high resolution publication-ready graphics -Practical advice on interpreting results in light of relatively recent views on threshold p-values, multiple testing, simultaneous confidence intervals, confounding adjustment, bootstrapping, and predictor variable selection -Suggestions of alternative approaches when a method's ideal inference conditions are unreasonable for one's dataThis book is invaluable for non-statisticians and applied statisticians who analyze and interpret real-world data. It could be used in a graduate level course for non-statistical disciplines as well as in an applied undergraduate course in statistics or biostatistics.

Moodie, Patricia F.

Research

Designed for researchers primarily interested in what their data are revealing, Applied Regression and ANOVA Using SAS presents rigorous statistical methods without burdening readers with matrix algebra and calculus. Each method is introduced by discussing its characteristic features, the reasons readers would want to use it, and its underlying assumptions. The book then guides readers in applying each method by describing a step-by-step approach, giving SAS code to implement the steps. The SAS code is annotated, which allows users to readily adapt it to their own data set.   In the step-by-step approach, readers are given practical advice on how to evaluate in depth whether the assumptions of a method are reasonable for their data set. The book also gives practical advice on interpreting results in the light of modern multiple testing procedures and simultaneous confidence intervals.  Readers are shown throughout the book how high resolution, publication-ready graphics associated with regression and ANOVA methods are produced with virtually no effort by the SAS user. Suggestions for navigating issues encountered in analyzing real-life data make this book invaluable to both non-statisticians and applied statisticians.

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

Biological Sciences

See the best price to sell, buy, or rent books by Johnson, Dallas E.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Johnson, Dallas E.

See the best price to sell, buy, or rent books by Johnson, Dallas E.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Analysis of Messy Data, Volume II: Nonreplicated Experiments

Analysis of Messy Data, Volume II

Analysis of Messy Data, Volume I: Designed Experiments

Applied Multivariate Methods for Data Analysts

Applied Regression and ANOVA Using SAS

Applied Regression and ANOVA Using SAS

Analysis of Messy Data, Volume III: Analysis of Covariance

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