Syllabus
--------
Instructor & TAs
~~~~~~~~~~~~~~~~
Instructors
^^^^^^^^^^^
`Jonathan Taylor `__
- Office: Sequoia Hall #137
- Phone: 723-9230,
- `Email `__
- 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
- `Email `__
- Office hours: T 1:00-3:00
- Location: 380-381U
TA : Matteo Sesia
- `Email `__
- Office hours: Th 8:30-10:30 AM
- Location: Sequoia Hall 207
TA : Jun Yan
- `Email `__
- Office hours: Th 1:00-3:00 PM
- Location: Sequoia Hall 105
TA : Jingyi Kenneth Tay
- `Email `__
- 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)
Final exam
~~~~~~~~~~
Following the Stanford
`calendar `__:
Tuesday, March 19, 2019 @ 8:30AM-11:30 AM, 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.
Jupyter for ``R``
~~~~~~~~~~~~~~~~~
In order to run the R notebooks below, you will need to install Jupyter
(easily done through `Anaconda `__ as well as
enable the ``R`` kernel:
.. code::
install.packages('IRkernel')
library(IRkernel)
IRkernel::installspec()
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.
Practice exam
-------------
You can find a practice exam `here `__ with
`solution `__.
.. raw:: html
Topics
------
1. Course introduction and review: `HTML `__,
`Jupyter `__,
`R-Markdown `__
2. Some tips on R: `HTML `__,
`Jupyter `__,
`R-Markdown `__
3. Simple linear regression:
`HTML `__,
`Jupyter `__,
`R-Markdown `__
4. Diagnostics for simple linear regression:
`HTML `__,
`Jupyter `__,
`R-Markdown `__
5. Multiple linear regression:
`HTML `__,
`Jupyter `__,
`R-Markdown `__
6. Diagnostics for multiple linear regression:
`HTML `__,
`Jupyter `__,
`R-Markdown `__
7. Interactions and qualitative variables:
`HTML `__,
`Jupyter `__,
`R-Markdown `__
8. Analysis of variance: `HTML `__,
`Jupyter `__,
`R-Markdown `__
9. Transformations and Weighted Least Squares:
`HTML `__,
`Jupyter `__,
`R-Markdown `__
10. Correlated errors: `HTML `__,
`Jupyter `__,
`R-Markdown `__
11. Bootstrapping regression:
`HTML `__,
`Jupyter `__,
`R-Markdown `__
12. Selection: `HTML `__,
`Jupyter `__,
`R-Markdown `__
13. Penalized regression:
`HTML `__,
`Jupyter `__,
`R-Markdown `__
14. Logistic: `HTML `__,
`Jupyter `__,
`R-Markdown `__
15. Poisson: `HTML `__,
`Jupyter `__,
`R-Markdown `__
16. 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 `__
.. raw:: html