Dallas E. Johnson George A., PH.D. Milliken Milliken
This practical tool for statisticians offers techniques and methods for analyzing non-standard or messy data sets that arise from experimental design situations. Topics discussed include analysis of variance techniques, such as one- and two-way analyses of variance and multiple-comparison procedures.
This practical tool for statisticians offers techniques and methods for analyzing non-standard or messy data sets that arise from experimental design ...
The authors describe the strategy for one- and two-way treatment struc tures with one and multiple covariates in a completely randomized desi gn structure. They present new methods for comparing models and sets o f parameters, including beta-hat models. They carefully investigate th e effect of blocking, explore mixed models, and present a new methodol ogy for using covariates to analyze data from nonreplicated experiment s. Analysis of covariance provides an invaluable set of strategies fo r analyzing data. With its careful balance of theory and examples, Ana lysis of Messy Data: Volume 3...
The authors describe the strategy for one- and two-way treatment struc tures with one and multiple covariates in a completely randomized desi gn struc...
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 procedures
Additional examples of split-plot designs and repeated measures...
A bestseller for nearly 25 years, Analysis of Messy Data, Volume 1: Designed Experiments helps applied statisticians and researche...
Designed for researchers and data analysts, this book presents rigorous statistical methods without burdening readers with mathematical theory. Readers see how linear regression and ANOVA can be used in biology, agriculture, health science, environmental science, fisheries, forestry, as well as the physical, social, and behavioral sciences. Each statistical topic is illustrated using detailed worked examples and relevant SAS procedures. The book emphasizes interpretation of analyses integrating graphical and statistical results and offers a detailed step-by-step guide for each analysis.
Designed for researchers and data analysts, this book presents rigorous statistical methods without burdening readers with mathematical theory. Rea...
