R Companion to Introductory Nonparametrics

Commentaires:
( 0 )
84 pages
Langue:
 English
The code and supporting text help develop understanding as well as provide computational tools for application to new data sets.
Ceci est un eBook gratuit pour les étudiants
Inscrivez-vous gratuitement
Tous les livres pour étudiants sont gratuits, sans exception. Moins de 15% de publicités dans les livres.
 
Premier mois gratuit
Abonnement pro gratuit les 30 premiers jours, puis $5.99/mois
Dernière publication
A propos de l'auteur

Paul Rippon originally trained as an engineer. Despite not enjoying statistics very much during undergraduate years he found great satisfaction in the improvement of processes in the steel industry through the application of statistical process control and total quality management methods. Paul...

Description
Content

This is a companion volume to INTRODUCTORY NONPARAMETRICS by J.C.W. Rayner. As a reader studies the theory and calculations for a particular non-parametric example in Rayner (2016), the identically numbered section in this volume will provide the necessary computer code in the R language to complete the analysis for that example. The code and supporting text help develop understanding as well as provide computational tools for application to new data sets.

About the author

Paul Rippon originally trained as an engineer. Despite not enjoying statistics very much during undergraduate years he found great satisfaction in the improvement of processes in the steel industry through the application of statistical process control and total quality management methods. Paul subsequently retrained as a statistician and has worked at the University of Newcastle, Australia since 1999 as a lecturer in statistics and currently as a statistical consultant assisting researchers with their data analysis.

  1. Preface 
  2. Using R 
  3. Examples and R Scripts
  4. A First Perspective on Nonparametric Testing
    1. The sign tests
    2. Runs tests
    3. The median test
  5. Nonparametric Testing in the Completely Randomised, Randomised Blocks and Balanced Incomplete Block Designs
    1. The Kruskal-Wallis test 
    2. The Friedman test
    3. The Durbin test
    4. Relationships of Kruskal-Wallis, Friedman and Durbin tests with ANOVA F tests
    5. Orthogonal contrasts: Page and umbrella tests
  6. Permutation Testing
    1. What is permutation testing and why it is important?
    2. Nonparametric multifactor ANOVA when the levels of the factors are unordered
    3. Revisiting some previous examples
  7. Bibliography