Analysis of Messy Data Volume 1

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A01=Dallas E. Johnson
A01=George A. Milliken
advanced statistical modeling for research
ANOVA Table
applied statistics
Author_Dallas E. Johnson
Author_George A. Milliken
cance
Category=PBT
Category=PDN
combinations
completely
Compound Symmetry
con
Con Dence Interval
confidence intervals
Constructing Con Dence Intervals
Covariance Parameter Estimate
dence
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
Error Sum
Estimable Functions
Estimated Standard Error
experimental design analysis
Experimental Unit
General Random Effects Model
Homogeneous errors
intervals
level
Linear Time
MANOVA methods
messy data
mixed model inference
Population Marginal Means
Proc Mixed Data
Randomized Complete Block
repeated measures methodology
SAS Mixed Code
SAS-GLM analysis
Satterthwaite Approximation
signi
Signi Cance Level
split plot experiment techniques
split-plot design
Strip Plot Design
structure
Test H0
treatment
Treatment Combination
Treatment Structures
variance component estimation
Variance Components
Variance Table
Xed Effects

Product details

  • ISBN 9781584883340
  • Weight: 1700g
  • Dimensions: 178 x 254mm
  • Publication Date: 02 Mar 2009
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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 the original publication.

New to the Second Edition

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

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.

George A. Milliken, Dallas E. Johnson

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