# Syllabus¶

## Instructor & TAs¶

### Instructors¶

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)

• 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: stats191-win1617-staff@lists.stanford.edu

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

Questions can also be posed on `canvas <http://canvas.stanford.edu>`__.

# Evaluation¶

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.

# Prerequisites¶

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.

# Project¶

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

# Topics¶

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