Quantitative Methods in Archaeology Using R
Part of Cambridge Manuals in Archaeology
- Author: David L. Carlson, Texas A & M University
- Date Published: July 2017
- availability: In stock
- format: Hardback
- isbn: 9781107040212
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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.
Read more- A hands-on approach to using archaeological data to answer research questions that will appeal to those who are interesting in applying quantitative methods to their data
- Readers can move directly from descriptions of methods to examples showing how to use them
- Descriptions of quantitative techniques include verbal description, key equations, and examples using real archaeological data
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×Product details
- Date Published: July 2017
- format: Hardback
- isbn: 9781107040212
- length: 452 pages
- dimensions: 262 x 184 x 26 mm
- weight: 1.12kg
- contains: 94 b/w illus.
- availability: In stock
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.
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