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 at TBD. Location is TBD by registrar.
Here is the syllabus. New this year: a bit more about causality.
Building 200 (History corner), Room 002 Monday, Wednesday and Friday 11:00 to 11:50
- Art Owen
- Sequoia Hall 130
- My userid is owenpelican on stanfordpelican.edu (remember to remove the waterfowl)
- Office: Friday 1:15
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.
Several students expressed interest in other books on regression. Here are some others that are relevant to this course. (Title + author + google should pull them up)
- ``Linear regression analysis'', Seber and Lee 2003, More theoretical.
- ``Regression analysis by example'', Chatterjee and Hadi 2012, Examples.
- ``Beyond ANOVA'', Rupert G. Miller Jr., 1986 (reprinted 1997), Covers classical material.
- ``Statistical methods'', Snedecor and Cochran, 1937 (many updates), very pragmatic, valuable for people with all prerequisites except experience with statistics.
- ``Plane answers to complex questions'', Christiansen, (4th ed. 2011), makes maximal use of linear algebra.
- ``Applied regression analysis'', Draper and Smith, (1966 ++), classic text. Your professors' professors learned or taught from this one.
Here are some scribed notes by Eric Min.
I am deeply indebted to Eric for carefully taking down these nice notes in a classroom with awkward sight lines, imperfect acoustics and my handwriting. There are a few errors and omissions due to those circumstances, but these notes are still the best thing for students who want to read ahead.Here are some further notes to supplement the class. These were written a few years ago and are a bit more formal than what I would write now, but they are useful for stat 305. Note: the PDFs have chapter numbers that are not unique. These notes cover things that are not in the course text.
Overview of 305| Review of relevant probability| Linear least squares| one way ANOVA| multi-way ANOVA
The problem sets are available to students registered in the class. I post them here as they are added.Be sure to give Axess a working email address:
The existence of a new problem set will be announced in class.
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.)
The exam is on Thursday December 11 from 8:30 am to 11:30 am. Do not book travel that conflicts with this date. University policy is that students may not register for two classes with exams at the same time.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.
John Ioannidis explains why published research findings are false. This is scary stuff. Almost nobody believes that the errors he talks about apply to them.
xkcd on correlation versus causation. First funny statistics joke I have ever seen. Lawyers have so many more to choose from.