# 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 .

# 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.