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Instructor & TAs

  • Professor:Jonathan Taylor
    • Office: Sequoia Hall #137
    • Phone: 723-9230,
    • Email
    • Office hours: T 1:00-3:00 or by appointment.
  • TA: Yunjin Choi
    • Office: Sequoia Hall 207
    • Email
    • Office hours: M 9:00-11:00
  • TA: Minyong Lee
    • Office: Sequoia Hall 207
    • Email
    • Office hours: W 9:00-11:00
  • TA: David Walsh
    • Office: Sequoia Hall 207
    • Email
    • Office hours: Th 4:00-6:00
  • Final exam: Following the Stanford calendar: Thursday, March 20 @ 3:30PM-6:30 PM, Mudd Chemistry Building LEC.

Schedule & Location

TTh 11:00-12:15, BraunLec


Computing environment

We will use R for most examples, with some python mixed in, particularly numpy and matplotlib.

All of the course notes are written with the ipython notebook, a great tool for interactive computing. Most R code is run through the R magic.


An introductory statistics course, such as STATS 60.

Course description

By the end of the course, students should be able to:

  • Enter tabular data using R.
  • Plot data using R, to help in exploratory data analysis.
  • Formulate regression models for the data, while understanding some of the limitations and assumptions implicit in using these models.
  • Fit models using R and interpret the output.
  • Test for associations in a given model.
  • Use diagnostic plots and tests to assess the adequacy of a particular model.
  • Find confidence intervals for the effects of different explanatory variables in the model.
  • Use some basic model selection procedures, as found in R, to find a best model in a class of models.
  • Fit simple ANOVA models in R, treating them as special cases of multiple regression models.
  • Fit simple logistic and Poisson regression models.


For those taking 4 units:

  • 5 assignments (50%)
  • data analysis project (20%)
  • final exam (30%) (according to Stanford calendar: Th 03/20 @ 3:30PM)

For those taking 3 units:

  • 5 assignments (70%)
  • final exam (30%) (according to Stanford calendar: Th 03/20 @ 3:30PM)



The data analysis project description describes what is needed for your project. It is due March 14, 2014.

Practice exam

You can find a practice exam here. Here are practice solutions.

Here is a 2nd practice exam with solutions.