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 owenpelican on stanfordpelican.edu
(remember to remove the seabirds)
- Office hour: Friday 11:00-12:00
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.
- Deep and thorough understanding of binary data, especially logistic regression.
- Competence in modeling categorical data including hands-on work getting data into a form suitable for analysis.
- Broad exposure to aspects of categorical data that one might otherwise miss.
1:30 to 2:20 Monday, Wednesday and Friday, starting Monday Jan 4
- 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" (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
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.
- Jeha Yang   jehaPenguin@stanford.edu
Office Hours: Friday 9:00-11:00   Sequoia Hall fishbowl
- Junyang Qian   junyangqPenguin@stanford.edu
Office Hours: Wednesday 11:30-1:30 & tba   Sequoia Hall fishbowl
Delete the Antarctic bird from the TAs' email
Be sure to give Axess a working email address:
- Homework: 4 to 6 problem sets   (65%)
- Midterm Friday February 12   (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.
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.)
Brown, Cai and Dasgupta's definitive treatment of
Interval estimation for
a binomial proportion
Chapters 1 and 12
including hierarchical models
of Richard McElreath's Bayesian statistics book
- Matan Gavish's
crash course on entropy and related ideas for categorical data analysis
- Gelman et al.
default prior for logistic regression
- Charles McCulloch's notes on
mixed models from JSTOR
- John D. Cook's notes
on the negative binomial distribution
- Mervyn Silvapulle's
article on existence of logistic regression MLEs
- Paul Komarek's
logistic regression on steroids (not his term)
Zipf and related things
Models for three sided coins
- Probabilistic models for document collections
- Bethany Percha's
on using text mining to find drug-drug interactions
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