Introduction to Robust Estimating and Hypothesis Testing, Fifth Edition is a useful ‘how-to’ on the application of robust methods utilizing easy-to-use software. This trusted resource provides an overview of modern robust methods, including improved techniques for dealing with outliers, skewed distribution curvature, and heteroscedasticity that can provide substantial gains in power. Coverage includes techniques for comparing groups and measuring effect size, current methods for comparing quantiles, and expanded regression methods for both parametric and nonparametric techniques. The practical importance of these varied methods is illustrated using data from real world studies. Over 1700 R functions are included to support comprehension and practice.
1. Introduction to Robust Estimation and Hypothesis Testing - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page iv 7. Contents - p. Page vi 8. Preface - p. Page vi 9. List of Illustrations - p. Page xi 10. List of Tables - p. Page xii 11. Chapter 1 : Introduction - p. Page xii 12. Chapter 2 : A Foundation for Robust Methods - p. Page 24 13. Chapter 3 : Estimating Measures of Location and Scale - p. Page 44 14. Chapter 4 : Inferences in the One-Sample Case - p. Page 106 15. Chapter 5 : Comparing Two Groups - p. Page 151 16. Chapter 6 : Some Multivariate Methods - p. Page 251 17. Chapter 7 : One-Way and Higher Designs for Independent Groups - p. Page 350 18. Chapter 8 : Comparing Multiple Dependent Groups - p. Page 465 19. Chapter 9 : Correlation and Tests of Independence - p. Page 539 20. Chapter 10 : Robust Regression - p. Page 575 21. Chapter 11 : More Regression Methods - p. Page 651 22. Chapter 12 : ANCOVA - p. Page 772 23. References - p. Page 826 24. Index - p. Page 826
Introduction to Robust Estimating and Hypothesis Testing, Fifth Edition is a useful ‘how-to’ on the application of robust methods utilizing easy-to-use software. This trusted resource provides an overview of modern robust methods, including improved techniques for dealing with outliers, skewed distribution curvature, and heteroscedasticity that can provide substantial gains in power. Coverage includes techniques for comparing groups and measuring effect size, current methods for comparing quantiles, and expanded regression methods for both parametric and nonparametric techniques. The practical importance of these varied methods is illustrated using data from real world studies. Over 1700 R functions are included to support comprehension and practice.
1. Introduction to Robust Estimation and Hypothesis Testing - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page iv 7. Contents - p. Page vi 8. Preface - p. Page vi 9. List of Illustrations - p. Page xi 10. List of Tables - p. Page xii 11. Chapter 1 : Introduction - p. Page xii 12. Chapter 2 : A Foundation for Robust Methods - p. Page 24 13. Chapter 3 : Estimating Measures of Location and Scale - p. Page 44 14. Chapter 4 : Inferences in the One-Sample Case - p. Page 106 15. Chapter 5 : Comparing Two Groups - p. Page 151 16. Chapter 6 : Some Multivariate Methods - p. Page 251 17. Chapter 7 : One-Way and Higher Designs for Independent Groups - p. Page 350 18. Chapter 8 : Comparing Multiple Dependent Groups - p. Page 465 19. Chapter 9 : Correlation and Tests of Independence - p. Page 539 20. Chapter 10 : Robust Regression - p. Page 575 21. Chapter 11 : More Regression Methods - p. Page 651 22. Chapter 12 : ANCOVA - p. Page 772 23. References - p. Page 826 24. Index - p. Page 826
