Quantitative Analysis I 

Anthropology 4840/7840  New Cabell Hall 187 
University of Virginia  Tuesday: 3:306:00 
Spring 2016  Fraser D.Neiman 

Description This course offers an introduction to quantitative data analysis in archaeology, anthropology, and related fields. Topics include statistical graphics, fundamentals of probability and probability distributions, regression and related linear and additive models, correlation, and the basics of Bayesian estimation. The course emphasizes practical, handson analysis and interpretation of real archaeological and anthropological data using R. It aims to empower students with the analytical skills required to evaluate empirical expectations from theoretical models, to discover unanticipated patterning and decipher its meaning, and to make the data analysis process open, transparent, and reproducible. Textbooks The required text for the course is Diez, David M., Christopher D. Barr and Mine CetinkayaRundel 2015 OpenIntro Statistics, Third Edition. Creative Commons. A free pdf version is avaiiable here. However,you may want to buy a hard copy, available from Amazon. It's a great deal at only ten dollars! If you are interested in using the Hadley Wickham's ggplot2 package for graphics, I highly recommend getting a copy of: Chang, Winston 2013 R Graphics Cookbook O'Reilly, New York. Here is the Amazon link. We will be looking at lots of archaeological data during the course. But if you can't get enough archaeology and think a text with archaeological examples would be helpful, you should consider purchasing: Drennan, Robert D. 2010 Statistics for Archaeologists: A Common Sense Approach, Second Edition. Springer, New York. Here is the Amazon link. Course Schedule Consult the course schedule for required reading. Additional reading will be available on Collab. 
Requirements Written work for the course includes Problems Sets and a Final Project. Problem Sets will be assigned nearly every week. Completed problem sets will be due the following week, at the beginning of class, when we will discuss them. Your write up should include not only your numerical results, illustrated with appropriate graphics, but also what you think they mean, both in statistical and substantive terms, and the code you used to produce them. Everyone should be prepared to contribute their insights and questions to the discussion. Late problem sets will not be accepted. The Final Project is your opportunity to use analytical methods we have learned to evaluate theoretically informed hypotheses about the dynamics reponsible for patterns in real data. You will have two options. The first requires you to identify a research problem and one or more sets of quantitative data that might be used to illuminate it. I encourage you to choose issues and data in which you have a personal research interest and, therefore, a basic familiarity with the current background literature. The data should contain information on several different variables that are of potential relevance to your problem. A key part of the project will be defending their relevance. This option require my approval of the topic and dataset(s) by the end of Spring Break. The second option is to work with models developed from signaling theory to identify and analyze patterns in the consumption of clay tobacco pipes by enslaved laborers in the 18thcentury Chesapeake. I will provide the background reading and data from DAACS. Your write up for the final project should not exceed 15 pages (doublespaced), exclusive of graphics. Grades of the course will be computed as follows: PS*.6 + FP*.3 + CD*.1, where PS=mean score for all problem sets, FP=score for the final project, and CD=mean score for contributions to class discussion. 
Office Hours, etc. My office is in the Monticello Archaeology Lab. A bit of a hike. But you are welcome to come up for a chat. Official office hours are from 8:0010:00, Monday morning. Or email me for an appointment. In addition, there will be an informal lab session at the Scholar's Lab in Alderman, Thursdays from 4:00 to 5:15, at which I'll be available to answer questions. 
Required Software: R and RStudio You will need to install both R and RStudio on your laptop, in that order. Plan on bringing your laptop with both programs installed to every class, including the first one. Here are links for the downloads: If you need help with the installations, try the kind folks at the Statlab, which is also a useful resource more generally. 
Helpful R Resources

Some Cool Stats Blogs The stats blogosphere is a lively place and well worth exploring. Here are some good starting places...

Datasets 