# Syllabus¶

## Instructor & TAs¶

### Instructors¶

- Office: Sequoia Hall #137
- Phone: 723-9230,
- Office hours: W 2:00-4:00
- Zoom office hours will be held Wednesday 12:00-2:00 just before on-campus office hours.

### Teaching Assistants & Office Hours¶

TA : Benjamin Seiler

- Office hours: T 1:00-3:00
- Location: 380-381U

TA : Matteo Sesia

- Office hours: Th 8:30-10:30 AM
- Location: Sequoia Hall 207

TA : Jun Yan

- Office hours: Th 1:00-3:00 PM
- Location: Sequoia Hall 105

TA : Jingyi Kenneth Tay

- Office hours: W 12:00-2:00
- Location: Sequoia Hall 207

# Email list¶

The course has an email list that reaches all TAs as well as the professors: stats191-win1819-staff@lists.stanford.edu

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

Questions can also be posed on gradescope.

# Evaluation¶

For those taking 4 units:

- 5 assignments (50%)
- data analysis project (20%)
- final exam (30%) (according to Stanford calendar: T 03/19 @ 8:30AM, Gates B1)

For those taking 3 units:

- 5 assignments (70%)
- final exam (30%) (according to Stanford calendar: T 03/19 @ 8:30AM, Gates B1)

# Schedule & Location¶

MWF 9:30-10:20, Gates B1

# Textbook¶

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

# 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¶

- Course introduction and review: HTML, Jupyter, R-Markdown
- Some tips on R: HTML, Jupyter, R-Markdown
- Simple linear regression: HTML, Jupyter, R-Markdown
- Diagnostics for simple linear regression: HTML, Jupyter, R-Markdown
- Multiple linear regression: HTML, Jupyter, R-Markdown
- Diagnostics for multiple linear regression: HTML, Jupyter, R-Markdown
- Interactions and qualitative variables: HTML, Jupyter, R-Markdown
- Analysis of variance: HTML, Jupyter, R-Markdown
- Transformations and Weighted Least Squares: HTML, Jupyter, R-Markdown
- Correlated errors: HTML, Jupyter, R-Markdown
- Bootstrapping regression: HTML, Jupyter, R-Markdown
- Selection: HTML, Jupyter, R-Markdown
- Penalized regression: HTML, Jupyter, R-Markdown
- Logistic: HTML, Jupyter, R-Markdown
- Poisson: HTML, Jupyter, R-Markdown
- Final Review: HTML, Jupyter, R-Markdown

# Assignments¶

- Assignment 1 HTML, Jupyter, R-Markdown
- Assignment 2 HTML, Jupyter, R-Markdown
- Assignment 3 HTML, Jupyter, R-Markdown
- Assignment 4 HTML, Jupyter, R-Markdown
- Assignment 5 HTML, Jupyter, R-Markdown