Stat 306a: Discrete data analysis
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
- Art Owen
- Sequoia Hall 130
- My userid is owenbuzzard on stanfordbuzzard.edu
(remember to remove the carrion eaters)
- Office hour: Tuesday 11:00am
11 to 12:15 Monday and Wednesday, starting Monday Jan 3
- Discrete distributions: Bernoulli, Binomial, Poisson, Multinomial
- Related continuous distributions: Beta, Dirichlet
- Chisquare tests
- Logistic regression
- Loglinear models for contingency tables
- Generalized linear models
- Bradley-Terry and related models
- Rasch and related models
- Predicting ordered and unordered categorical values
- market basket analysis
- sequence similarity
- information retrieval
The main text is "Categorical Data Analysis" (second 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
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
I'm assuming that you already know how to use R. After all
Stat 305 is a prerequisite and it is R based.
- Matan Gavish   firstname.lastname@example.org
Office Hours: Wed 10-11 Thur 12:15-1:15   Sequoia Hall 216
- Yueh-Wen (Olivia) Liao   email@example.com
Office Hours: Mon 1-2 Fri 12:30-1:30   Sequoia Hall 242
Delete the Antarctic bird from the TA's email
Be sure to give Axess a working email address:
- Homework: 4 to 6 problem sets   (65%)
- Take home final:   (35%)
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.
HW turned in on the due date but after class ends
is one day late. The next day is two days late and
late if it is not turned in in class
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.)
- Matan Gavish's
crash course on entropy and related ideas for categorical data analysis
- Paul Komarek's
logistic regression on steroids (not his term)
Wikipedia on Zipf's law
Article on Zipf's law on Gutenberg books (Thanks to David Gleich)
Models for three sided coins
- Probabilistic models for document collections
Elizabeth Purdom's R tutorial
Website for Agresti's book
- Laura Thompson's guides to R/Splus computing for Agresti's book
Notes on generalized linear models
Wikipedia pages on some course related distributions
In R: qbinom, pbinom, dbinom, rbinom
In R: qpois, ppois, dpois, rpois
Hypergeometric In R: qhyper, phyper, dhyper, rhyper
In R: qnegbin, pnegbin, dnegbin, rnegbin of library(MASS)
In R: qbeta, pbeta, dbeta, rbeta
Problems (closed for the season)