STATISTICS 545 - DATA ANALYSIS

Instructor: Paul Gustafson

The course outline is here in latex format.

The text for the course is

Modern Applied Statistics with S-Plus, by Bill Venables and Brian Ripley, 1994.

Ripley's homepage has links to information about the book and associated software.

In order to hone our data analysis skills, we will require lots of data sets to analyze! Here are some sources of data sets

• The software which accompanies the textbook includes all the data sets mentioned in the book, as well as some others. See Appendix A of the text for a list.
• StatLib , is a general purpose server for the statistical community. There are several sites on Statlib where data sets are available, including
  • The datasets archive contains many varied data sets, as well as collections of data sets from some books.
  • The Data and Story Library is an "online library of datafiles and stories that illustrate the use of basic statistics methods."
  • There are several other sites on StatLib which contain data sets. Check out the index on the StatLib home page.
• A Data Sources page is maintained at the University of Nevada.
• UBC Statistics members can access some data sets available on the department network. Check the department gopher under computing.

If you are craving more information about S, try the FAQ . As well, back at Statlib one can find the S-archive, which contains much S software, as well as the archives of the S-news mailing list.

Some students asked for electronic copies of the S-Plus examples from class. Here they are. Some are better documented with comments than others---CAVEAT EMPTOR!

OUTSIDE BROWSERS: these probably won't make much sense without the class notes and handouts.

• Session 1: Graphics examples using the Boston housing data set (available from statlib data archive).
• Session 2: Kernel density estimation examples using simulated data sets.
• Session 3: Bootstrapping examples using simulated data sets.
• Session 4: Model formulae examples.
• Session 5: Regression analysis of the "Mercury in Fish" data set.
• Session 6: Poisson overdispersion examples.
• Session 7: Robust regression examples.
• Session 8: Regression tree examples.
• Session 9: Survival analysis examples (Kaplan-Meier estimator and proportional hazards model).
• Session 10: Classification example, using the forensic glass data set.