Statistics
501/Psychology 612:
Introduction to
Nonparametrics
and Log-Linear
Models
Paul R. Rosenbaum
Professor,
Statistics Department, Wharton School
Description
Statistics 501/Psychology 612 is a second course in statistics for PhD students in the social, biological and business sciences. It covers nonparametric statistical methods for data that are not Normally distributed and methods for discrete data, such as log-linear and logit models. Students should have taken an undergraduate course in statistics prior to Statistics 501. Statistics 500 is suggested but not required as background.
Course Materials
The texts are Nonparametric Statistical Methods by M. Hollander and D. Wolfe, second edition, John Wiley and Sons, and S. Fienberg, The Analysis of Cross-Classified Categorical Data. There is also a bulkpack consisting of photocopies of computerized data analysis which is available from Wharton Reprographics. Students make extensive use of the computer to analyze data. There is a midterm on nonparametrics, a final on discrete data, and 5-page report on an analysis of data you select. In addition, there are ungraded homeworks.
Nonparametrics: Nonparametric tests, estimates, and
confidence intervals, nonparametric analysis of variance, and correlation.
Discrete data: Log-linear, logit and ordinal logit models.