Introduction to Robust Estimation and Hypothesis Testing
This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context. Author Rand Wilcox includes chapter exercises and many real-world examples that illustrate how various methods perform in different situations.
Introduction to Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications of modern, robust methods which can greatly enhance our chances of detecting true differences among groups and true associations among variables.
* Covers latest developments in robust regression
* Covers latest improvements in ANOVA
* Includes newest rank-based methods
* Describes and illustrated easy to use software
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If your background in mathematics isn't strong, this book may seem daunting. Stick with it. (Perhaps read Wilxocx's FUNDAMENTALS OF MODERN STATISTICAL METHODS first.) The important thing is that the book being reviewed here is one of the greatest bargains a quantitative researcher could possibly get hold of. What you get -- provided that you download the statistical freeware R and read a couple of tutorials about it -- is an astounding treasure trove of keys to modern statistical procedures.
Chapter 1 Introduction
Chapter 2 A Foundation for Robust Methods
Chapter 3 Estimating Measures of Location and Scale
Chapter 4 Confidence Intervals in the OneSample Case
Chapter 5 Comparing Two Groups
Chapter 6 Some Multivariate Methods
Chapter 7 OneWay and Higher Designs for Independent Groups