University of Minnesota, Twin Cities School of Statistics Stat 5601 Rweb
We will be using computers in the course. The default statistical computing package will be R and its web interface Rweb, which can be used in any web browser. You are permitted to use any other computer statistics package with which you are familiar and we can have installed in the computing lab (so you can use it for tests), but the instructor and teaching assistant may not be able to help you with it.
R is a general purpose computing language designed especially for statistics. You can do any computational problem in R, but what it is really good at is statistics.
R is free software (both free as in "free beer" and free as in "free speech"). If you want to use it at home, you can download it from CRAN (the Comprehensive R Archive Network). It is available for Microsoft Windows and all versions of UNIX (including Linux). A Macintosh port is there but may be tricky to install. You do not have to download R if you don't want to. R is installed in the computers in the lab (Room B53, Ford Hall), and R can be used over the web on any computer that runs a web browser (see the section on Rweb).
R is a free implementation of the S statistical computing environment, originally developed at Bell Labs around 1980. S is now only available as the S-Plus proprietary (non-free, in fact fairly expensive, although there are somewhat cheaper student editions) software package. If you don't have either R or S-Plus, there is no point in getting S-Plus for the needs of this course. If you already have S-Plus, you can use it instead of R, although you may have to install (or have a system administrator install) the bootstrap package we will use in the second half of the course.
Because R is free software, it can be made to run over the web.
Many examples we will use in this course are inserted right in the course web pages. For an example (not particularly related to nonparametrics), click on the "Submit" button below, which runs Rweb to make a scatter plot, do a linear regression, and draw the regression line for the data set in the text file http://www.stat.umn.edu/geyer/somedata entered in the "dataset URL"box.
After you've looked at the plot, use the "Back" button on your browser to return here.
Note that this link is on the menu at the top of most of the course web pages.