- Professor:Jonathan Taylor
- Office: Sequoia Hall #137
- Phone: 723-9230,
- Office hours: T 11:00-1:00 or by appointment.

- TA: Bhaswar Battacharya
- Office: Sequoia Hall #206
- Office hours: M 11:00-1:00

- TA: Murat Erdogdu
- Office: Sequoia Hall #206
- Office hours: W 11:00-1:00

- TA: Alex Chin
- Office: Sequoia Hall #207 (Bowker)
- Office hours: Th 4:00-6:00

- Final exam: Following the Stanford calendar: Wednesday, March 18 @ 7:00PM-10:00 PM, Room TBA.

TTh 2:15-3:30, 380-380C

- Regression Analysis by Example, Chaterjee, Hadi & Price.

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.

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
bestmodel 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: W 03/18 @ 7:00PM)

For those taking 3 units:

- 5 assignments (70%)
- final exam (30%) (according to Stanford calendar: W 03/18 @ 7:00PM)

- Course introduction and review (slides, html, pdf, ipynb)
- Some tips on R (html, pdf, ipynb)
- Simple linear regression (slides, html, pdf, ipynb)
- Diagnostics for simple linear regression (slides, html, pdf, ipynb)
- Multiple linear regression (slides, html, pdf, ipynb)
- Diagnostics for multiple linear regression (slides, html, pdf, ipynb)
- Interactions and qualitatitve variables (slides, html, pdf, ipynb)
- Analysis of variance (slides, html, pdf, ipynb)
- Transformations (slides, html, pdf, ipynb)
- Correlated errors (slides, html, pdf, ipynb)
- Selection (slides, html, pdf, ipynb)
- Penalized regression (slides, html, pdf, ipynb)
- Logistic (slides, html, pdf, ipynb)
- Poisson (slides, html, pdf, ipynb)
- Final Review (slides, html, pdf, ipynb)