Stat 204: Sampling

Overview

This course is about sampling methods. Sampling is often the source of our data. Gathering it well is important to get reliable answers. Gathering poorly makes good answers nearly impossible. The course announcement lists the topics for this course. It includes both classical survey sampling methods as well as some more modern things.

Goals

  1. Learn basic strategies and their consequences for sampling from finite populations.
  2. Learn about methods on the frontier of sampling; it goes way beyond surveys.
  3. Actually do a sampling project.

Classes

Herrin Hall, T-185
Monday and Wednesday 1:30 to 2:50

Instructor

Art Owen
Sequoia Hall 130
My userid is owenpelican on stanfordpelican.edu (remember to remove the waterfowl)
Office: Tuesday 11:00

TAs

Notes and texts

The class text is "Sampling", third edition by Steven K. Thompson.

Midterm

There will be a midterm in class on April 27. This midterm is closed book and closed to notes too. Just you and your blue book and a pen or pencil. The midterm will count 30%. There is no final.

Problems

They will go in this coursework page. Sign in with your sunet ID.
Some R data sets
Students are expected to use R to do the problem sets.

Links

Links to related/interesting material will go here.
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 204 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.)