A01=Jr.
A01=Jr. Miller
A01=Rupert G. Miller
Actual Significance Level
advanced statistical data analysis
alternatives
ANOVA Estimate
ANOVA Table
ANOVA Type Statistic
Author_Jr.
Author_Jr. Miller
Author_Rupert G. Miller
Bonferroni Intervals
bootstrap techniques
Category=PBT
Category=PBW
confidence
Delta Method Estimate
distribution
Empirical Bayes Estimators
empirical Bayes methods
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Heavy Tailed Distribution
heavy-tailed
hypothesis
interval
jackknife resampling
James-Stein estimator
Jr.
Linear-2 Contrast Test
Mixed Effects Model
monotone
Monotone Alternatives
Nested Random Effects Model
Normal Theory Procedures
Null Hypothesis Ho
Permutation Test
plots
probit
Probit Plots
random effects modeling
Regression Model
Rupert G. Miller
Simultaneous Confidence Intervals
Steel Dwass Test
Studentized Range
Unit DNA
variance analysis
VDT.
Vice Versa
Wilcoxon Rank Test
Wilcoxon Statistic
Product details
- ISBN 9780412070112
- Weight: 780g
- Dimensions: 156 x 234mm
- Publication Date: 01 Jan 1997
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Renowned statistician R.G. Miller set the pace for statistics students with Beyond ANOVA: Basics of Applied Statistics. Designed to show students how to work with a set of "real world data," Miller's text goes beyond any specific discipline, and considers a whole variety of techniques from ANOVA to empirical Bayes methods; the jackknife, bootstrap methods; and the James-Stein estimator.
This reissue of Miller's classic book has been revised by professors at Stanford University, California. As before, one of the main strengths of Beyond ANOVA is its promotion of the use of the most straightforward data analysis methods-giving students a viable option, instead of resorting to complicated and unnecessary tests.
Assuming a basic background in statistics, Beyond ANOVA is written for undergraduates and graduate statistics students. Its approach will also be valued by biologists, social scientists, engineers, and anyone who may wish to handle their own data analysis.
Rupert G. Miller Jr., University of Stanford, California, USA.
