Stat 305: Linear Models (and more)


This course is about regression methods. In regression we're working primarily with real valued responses. The main tool for regression is the linear model, in all it's glory ranging from the humble one sample t test to more elaborate methods like splines and wavelets. We also look at competing methods that are sometimes better than linear regression, because the focus is on the problems not the tools.

There will be about 5 problem sets and a final exam. Students are expected to use R to do the problem sets.

The final exam is on Monday December 9 from 3:30 to 6:30. Location is TBD by registrar.

Here is the syllabus. New this year: a bit more about causality.




Art Owen
Sequoia Hall 130
My userid is owenpelican on (remember to remove the waterfowl)
Office: Wed 11am



The course texts are usually online. The links change from time to time. If the ones below don't work, try going through the Stanford library online catalog using your sunet ID. The main text is "Applied Linear Regression" by Weisberg
It may be available online to Stanford users here or here .

The supplementary text is ``Introductory Statistics with R'' by Peter Dalgaard.
Available online from Stanford accounts here.

That book explains how to use R. If you already know how to use R you don't need to buy it. There are R tutorials below as well.


Problems (password will be given in class)
...also the existence of a new problem set will be announced in class

Be sure to give Axess a working email address:
I expect to send a small number of important emails about problem sets and the homework there. Most other announcements will be made in class. If you email me about the class, be sure to have stat 305 in your subject line. Otherwise, your email won't show when I search for course related emails.
Late penalties apply:
We will count days late on each problem set. Each day late is penalized by 10% of the homework value. Homework more than 3 days late will ordinarily get 0. If you're travelling, you can email a pdf file. For sickness, interviews and other events, up to 3 late days total are forgiven at the end of the quarter. (Work late enough to get zero does not get redeemed though.)

Final Exam 35%

The exam is on Monday December 9 from 3:30 to 6:30 pm. Do not book travel that conflicts with this date.

The exam is closed book and is also closed to notes, calculators and phones. You may be asked to supply short derivations or proofs, to give advice on how to handle some hypothetical data, or diagnose a problem based on some regression output.

Supplementary materials

Big picture

Statistical material

Subject matter material