Stat 306a: Discrete data analysis

Overview

Stat 305 looked at regression models for real valued response variables. Things change when the response variable we're looking at is discrete. The binary case is the simplest, and for it we will study logistic regression, possibly the most important discrete data analysis method. Related methods are available for multicategory (ordered or unordered) responses. Loglinear models are there for multivariate discreted data in which we don't necessarily wish to identify a response variable.

Counted data are becoming ever more important in the age of the Internet. Information retrieval made significant progress when it adopted the point of view that documents can be represented as large sparse discrete data vectors. Companies involved in e-commerce develop enormous log files of data and simply counting what happens (and what things happen together) can yield richly informative data. In the second portion of the course we'll look at some of these topics and related methods. Machine translation of natural languages (not covered here) is also dominated now by data intensive methods with discrete data.

Instructor

Art Owen
Sequoia Hall 130
My userid is owenpelican on stanfordpelican.edu (remember to remove the seabirds)
Office hour: Friday 11:00-12:00

Goals

  1. Deep and thorough understanding of binary data, especially logistic regression.
  2. Competence in modeling categorical data including hands-on work getting data into a form suitable for analysis.
  3. Broad exposure to aspects of categorical data that one might otherwise miss.
We only have one quarter. The class should be deep and it should also be broad. The compromise is go into depth on key topics, while learning basics of related ones.

Classes

1:30 to 2:20 Monday, Wednesday and Friday, starting Monday Jan 4

Topics

Texts

The main text is "Categorical Data Analysis" (third edition) by A. Agresti. We will use it for the first half to two thirds of the course. For the rest of the course we'll look at ways that categorical data are being used in real world large scale applications. For that we'll switch to research articles and the supplementary text, "Learning Python", by Lutz and Ascher. That book explains how to use Python. If you already know how to use Python you don't need to buy it. You might also find you like another book better, but this one works well. Python is good for generating discrete data from raw sources like text. Then you can dump discrete data to a file and analyze it in R. Over time you might end up doing more in python and less in R. Python has a rich set of libraries. I'm assuming that you already know how to use R. After all Stat 305 is a prerequisite and it is R based.

TAs

Evaluation

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.
Late penalties apply:
We will count days late on each problem set. There is no late penalty for work turned in in class on the due date. Work turned in within 24 hours of that is 1 day late, 48 hours for 2 days late, etc. 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.)

Supplementary materials