- Professor:Jonathan Taylor
- Office: Sequoia Hall #137
- Phone: 723-9230,
- Office hours: T 1:00-3:00 or by appointment.

- TA: Yunjin Choi
- Office: Sequoia Hall 207
- Office hours: M 9:00-11:00

- TA: Minyong Lee
- Office: Sequoia Hall 207
- Office hours: W 9:00-11:00

- TA: David Walsh
- Office: Sequoia Hall 207
- 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.

TTh 11:00-12:15, BraunLec

- 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: 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.

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

Here is a 2nd practice exam with solutions.

- 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)