R Statistical Computing Environment
We will primarily be using the R statistical computing environment in this course. R is a freely available, open-source version of S/S-plus. While most S/S-plus code will run in R, there are several differences. Therefore, it is recommended that all computing for the course be done in R.

Previous experience with statistical computing is a prerequisite for this course. However, knowledge of R or S/S-plus is not expected. Demonstrations of analyses in R will be given during lecture, and example code will be provided for lecture examples as well as with some homework assignments. In addition to this example code, it is highly recommended that the R manual and online help system be used as a supplement.

Students not familiar with R may want to work through an R tutorial prepared by Hongfei Li. Data sets used in the tutorial can be downloaded here: toxicity.dat, lakes.dat

R is available in the Department of Statistics Student Computer Laboratory, which is open only to Statistics students. R can also be downloaded to personal computers for no cost. Students who do not have access to the computer lab and have difficulty accessing R should speak to the instructor.

Students using R on their personal computers may want to download Tinn-R, an R code editor -- see the installation and configuration instructions (prepared by Dale Rhoda).
NOTE: You do not need to use Tinn-R to edit R code; you can use Notepad, as well as other text editors available in Windows.

GGobi Data Visualization System
In addition to R, we will occasionally use the GGobi Data Visualization System, an interactive visualization system for multivariate data, in class. You will NOT be required to do any analyses outside of class using GGobi. If you are interested in using the software, it freely available and can be download from the GGobi website. Examples of analyses using GGobi will be given in lecture and the GGobi manual should be consulted for more details.