There are no required texts for this course, however, it is strongly recommended that you purchase a reference book for each of LaTeX, S-Plus, and C. Which book is up to you. Some suggestions are:

• Modern Applied Statistics with S-Plus by William Venables and Brian Ripley, published by Springer-Verlag. Highly recommended for this and other courses, and for future use as a reference text.
• LATEX: A Document Preparation System -- User's Guide and Reference Manual by Leslie Lamport, published by Addison-Wesley. This is my personal favorite, although many other LaTeX guides are equally good and are essentially exchangeable. For example,
• The LaTeX Companion by M Goossens, F Mittelbach and A Samarin, published by Addison-Wesley. This is another popular guide to LaTeX.
• A Guide to LaTeX -- Document Preparation for Beginners and Advanced Users by H. Kopka and P. Daly, published by Addison-Wesley. Yet another popular guide to LaTeX.
• Math into LaTeX -- An Introduction to LaTeX and AMS-LaTeX by George Gratzer, published by Birkhauser. Still yet another LaTeX book.
• Numerical Computation in C by Robert Glassey, published by Academic Press. This presents an introduction to C from the perspective of applied mathematics, a much more relavent perspective than the traditional computer science one.
  
• Teach Yourself C by Herbert Schildt, published by Osborne McGraw-Hill, an elementary introduction to the C programming language.
  
  
• Additional resources:
  • The S-Plus manuals. Duke Statistics has several copies of the S-Plus manuals which provide useful discussion of lots of statistical models and methods as well as computing details.
  • As useful as (if not more than) manuals is the on-line help system: in S-Plus simply type

     help.start() 

    to fire this up, then search by keywords. This on-line help system is excellent and indispensible.
  • Check the computing link for other links to support material on unix, editors, S-Plus, etc.

Basic probability and statistics:

There are many introductory statistics texts that cover essentially the same range of basic probability theory and statistical models and methods. A couple of really good ones you might consult from time to time are noted below. In addition, a lot of relevant material at an introductory level is available in some of the notes -- much won't be explictly covered, but you should find lots of the material there useful and it is easy to browse.

• Probability and Statistics by Morris H DeGroot, published by Addison Wesley (2nd Edn). An excellent traditional (but now older text) on basic theory and methods -- the course text for the parallel course STA 213. This covers basic elements of both Bayesian and non-Bayesian approaches to statistics and has been a standard introductory text for many years. Many other texts cover similar material on the non-Bayesian side
• Statistics: Theory and Methods by Don Berry and Bernard Lindgren, published by Duxbury. An excellent standard introductory text, written by one Bayesian and one non-Bayesian statistician.

More specifically on Bayesian ideas and methods:

• Bayesian Data Analysis by Andrew Gelman, John B Carlin, Hal S Stern and Don B Rubin, published by Chapman & Hall. It is a more advanced statistical modelling text that goes beyond the scope of this course, but it is a truly excellent text for both statistical modelling and applications, is full of good reading on concepts, and has many examples. It is used in several follow-on statistics courses in Duke Statistics.
• Bayesian Approach to Interpreting Archaeological Data by Caitlin Buck, William Cavanagh and Cliff Litton, published by Wiley. Archaeology? Sounds curious, but this is an excellent statistics book! Chapters 3-7 provide an excellent introduction to basics of probablity and statistical modelling from a Bayesian perspective. The book has lots of applied material, mostly from archaeology, that is worth reading.