Quantitative Methods in Archaeology Using R

by David L. Carlson

Overview

Quantitative Methods in Archaeology Using R is the first hands-on guide to using the R statistical computing system written specifically for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with applications to real archaeological data showing how to compute descriptive statistics, create tables, and produce a wide variety of charts and graphs. Part II addresses the major multivariate approaches used by archaeologists, including multiple regression (and the generalized linear model); multiple analysis of variance and discriminant analysis; principal components analysis; correspondence analysis; distances and scaling; and cluster analysis. Part III covers specialized topics in archaeology, including intra-site spatial analysis, seriation, and assemblage diversity.

Product Details

ISBN-13:	9781107040212
Publisher:	Cambridge University Press
Publication date:	06/26/2017
Series:	Cambridge Manuals in Archaeology
Edition description:	New Edition
Pages:	452
Product dimensions:	7.24(w) x 10.31(h) x 1.02(d)

About the Author

David L. Carlson is a Professor of Anthropology at Texas A & M University, where he has been teaching quantitative methods and the R statistical system to anthropology graduate students for eight years. His research focuses on the application of quantitative methods to discover and understand patterning in the distribution of artifacts on archaeological sites. He is a co-author of Clovis Lithic Technology (2011).

Table of Contents

Introduction; 1. Organization of the book; Part I. R and Basic Statistics: 2. Introduction to R; 3. Looking at data – numerical summaries; 4. Looking at data – tables; 5. Looking at data – graphs; 6. Transformations; 7. Missing values; 8. Confidence intervals and hypothesis testing; 9. Relating variables; Part II. Multivariate Methods: 10. Multiple regression and generalized linear models; 11. MANOVA and canonical and predictive discriminant analysis; 12. Principal components analysis; 13. Correspondence analysis; 14. Distances and scaling; 15. Cluster analysis; Part III. Archaeological Approaches to Data: 16. Spatial analysis; 17. Seriation; 18. Assemblage diversity; 19. Conclusions; 20. References.