In many theoretical mathematics courses, little computation is required. However, in both the undergraduate and graduate mathematical statistics courses that I teach, students need to be able do these tasks using software.

1. Graph even very complicated functions, in order to find maxima / minima.
2. Solve equations numerically.
3. Do computations for data analysis and inference for all all the usual statistical techniques in an introductory statistics course.
4. Do simulations, such as simulating the sampling distribution of a statistic.

In this graduate theoretical statistics course, we will discuss some other computational tasks using software. I will provide scripts and instructions in R for these tasks listed above and for other computational tasks in the course. Students are not expected to know R before the course begins, and will not learn to be experts in R during the course. However, I expect students to be able to run the scripts I provide and adapt them to some extent. Students will work in groups on any work requiring them to use R.

It is fine - a good thing, indeed - for students to know how to use other software besides R to do any or all of these tasks. I am emphasizing the use of R because it is freely available so that everyone can have easy access to it, and it will be useful for everyone in the class to be able to communicate about the computational issues using the same language.

R is a free package which is available on several different platforms. Information about downloading and setting it up will be provided.

Last updated August 27, 2009 . Mary Parker, parker@math.utexas.edu