Instructor & TAs


Jonathan Taylor

  • Office: Sequoia Hall #137
  • Phone: 723-9230,
  • Email
  • Office hours: T 1:00-3:00

Teaching Assistants & Office Hours

TA : Xiaoying Tian

  • Email
  • Office hours: Th 10:00-12:00
  • Location: Sequoia Hall #207

TA : Jeha Yang

  • Email
  • Office hours: Th 1:00-3:00
  • Location: Sequoia Hall #105

TA : Jaime Roquero Gimenez

  • Email
  • Office hours: W 3:00-5:00
  • Location: Fishbowl (upstairs Sequoia Hall)

TA : Nikos Ignatiadis

  • Email
  • Office hours: M 2:30-4:30
  • Location: Fishbowl (upstairs Sequoia Hall)

Email list

The course has an email list that reaches all TAs as well as the professors:

As a general rule, you should send course related to this email list.

Questions can also be posed on `canvas <>`__.


For those taking 4 units:

  • 5 assignments (50%)
  • data analysis project (20%)
  • final exam (30%) (according to Stanford calendar: T 03/21 @ 8:30AM)

For those taking 3 units:

  • 5 assignments (70%)
  • final exam (30%) (according to Stanford calendar: T 03/21 @ 8:30AM)

Final exam

Following the Stanford calendar: Tuesday, March 21, 2017 @ 8:30AM-11:30 AM, TBA.

Schedule & Location

MWF 9:30-10:20, NVIDIA Auditorium


Computing environment

We will use R for most calculations. Class notes are in the form of jupyter notebooks.


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.


The data analysis project description describes what is needed for your project.


  1. Course introduction and review: HTML, notebook
  2. Some tips on R: HTML, notebook
  3. Simple linear regression: HTML, notebook
  4. Diagnostics for simple linear regression: HTML, notebook
  5. Multiple linear regression: HTML, notebook
  6. Diagnostics for multiple linear regression: HTML, notebook
  7. Interactions and qualitative variables: HTML, notebook
  8. Analysis of variance: HTML, notebook
  9. Transformations: HTML, notebook
  10. Correlated errors: HTML, notebook
  11. Selection: HTML, notebook
  12. Penalized regression: HTML, notebook
  13. Data snooping: HTML, notebook
  14. Logistic: HTML, notebook
  15. Poisson: HTML, notebook