PS 599 --- Fall 2004

This is the website for Statistical Methods in Political Research, which is this introductory course in applied statistics for graduate students in the Political Science department of the University of Michigan.

Syllabus here.

Section webpage TBA.

Announcements

Readings that are not online are available for you to copy on the door of CPS 4253 or on CTools .

An example of how a confidence interval is the acceptance region of a set of null hypothesis tests is shown in this pdf . If you want to play with this yourself, you can download the Sweave file here .

Homework

Links on Scientific Computing

Code from the Handouts

R-code for the handout on the Weak Law of Large Numbers and the Central Limit Theorem here.

Learning Stata

• UCLA has a very nice set of notes and links here

Learning R

• The R manuals and help pages are here
• More documents about learning R here
• Robert Anderson's ICPSR course page with great lectures using R is here
• Julian Faraway's great book on using R is here
• Mahmood Arai's "Brief Guide to R for Beginners in Econometrics" here
• UCLA has a very nice set of notes and links here

Learning LaTeX

• For a basic book to get started:
  Helmut Kopka and Patrick Daly. 2004. Guide to LaTeX, Fourth Edition.
• For a more advanced reference guide:
  Frank Mittelbach, Michel Goossens, Johannes Braams, David Carlisle, Chris Rowley. 2004. LaTeX Companion, Second Edition.

Readings and Supplementary Materials

Achen, Chris. 2002. ``TOWARD A NEW POLITICAL METHODOLOGY: Microfoundations and ART.'' Annual Review of Political Science. Volume 5, Page 423-450, Jun 2002

King, Gary. ``How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science''. American Journal of Political Science, Vol. 30, No. 3 (August, 1986): Pp. 666-687.

Cohen, Jacob. 1994. `` The Earth is Round (p<.05) '' American Psychologist , 49:12, 997-1003.

Measurement Theory

This is a nice page tthat summarizes and also provides cites to the canonical texts.

Data Visualization

Lots of examples of data visualization: here .

Resampling and the Bootstrap

Bob Stine's lectures for his mini-course on the bootstrap are here

A great book on the topic, Bootstrap Methods and Their Application by A.C. Davison and D.V. Hinkley.

And, of course, An Introduction to the Bootstrap by Efron and Tibshirani.

Some articles by Bradley Efron

Computers and the Theory of Statistics: Thinking the Unthinkable. Bradley Efron. SIAM Review, Vol. 21, No. 4. (Oct., 1979), pp. 460-480.
Abstract
This is a survey article concerning recent advances in certain areas of statistical theory, written for a mathematical audience with no background in statistics. The topics are chosen to illustrate a special point: how the advent of the high-speed computer has affected the development of statistical theory. The topics discussed include nonparametric methods, the jackknife, the bootstrap, cross-validation, error-rate estimation in discriminant analysis, robust estimation, the influence function, censored data, the EM algorithm, and Cox's likelihood function. The exposition is mainly by example, with only a little offered in the way of theoretical development.

A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. Bradley Efron; Gail Gong. The American Statistician, Vol. 37, No. 1. (Feb., 1983), pp. 36-48.
Abstract
This is an invited expository article for The American Statistician. It reviews the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule. The presentation is written at a relaxed mathematical level, omitting most proofs, regularity conditions, and technical details.

Interpretations of Probability

Controversies in the Foundations of Statistics Bradley Efron. The American Mathematical Monthly, Vol. 85, No. 4. (Apr., 1978), pp. 231-246.

Why Isn't Everyone a Bayesian? B. Efron The American Statistician, Vol. 40, No. 1. (Feb., 1986), pp. 1-5.
Abstract
Originally a talk delivered at a conference on Bayesian statistics, this article attempts to answer the following question: why is most scientific data analysis carried out in a non-Bayesian framework? The argument consists mainly of some practical examples of data analysis, in which the Bayesian approach is difficult but Fisherian/frequentist solutions are relatively easy. There is a brief discussion of objectivity in statistical analyses and of the difficulties of achieving objectivity within a Bayesian framework. The article ends with a list of practical advantages of Fisherian/frequentist methods, which so far seem to have outweighed the philosophical superiority of Bayesianism.