Department of Statistics

- Organizational Meeting for all Spring term courses whose times are not listed below: Thursday, 9 January at 10:00, Room 107, 24 Hillhouse Avenue. Those interested in attending one of the courses but unable to be present at this meeting should inform Mrs. Kennedy beforehand and submit their schedules.
- The Department has consolidated training in the use of the S-plus statistical computing environment into the course Statistics 200b. It is required for Statistics 230b, 312a, 361a; it is recommended for 242 and higher level courses involving analysis of data. S-plus can be used at several different levels: a high-powered calculator; a simple statistics package, for the fitting of regressions and the production of simple statistical summaries; a powerful language for statistical graphics; and as an environment for the production of new statistical tools.
- Courses whose numbers end with
**a**are taught in the fall; courses whose numbers end with**b**are taught in the spring.

Fall courses: 123/523, 200, 241/541, 312/612, 361/661, 610, 625, 685, 690,

Spring courses: 200, 230/530, 242/542, 251/551, 364/364, 600/330, 606, 618, 626,

**Statistics 123a, Introduction to Statistical Methods and Probabilistic
Reasoning **
* Cross-listing: Statistics 523a *

Instructor: Mr. A. Barron.

Basic concepts of statistical methods shown through examples of statistical practice. Introduction to probabilistic reasoning, hypothesis testing, regression. Some use of computers for data analysis.

Time: Tue., Thur., 1:00-2:15

**Statistics 200La and 200Lb (66200), Statistical Computing Laboratory **

Instructor: Mr. D. Pollard.

This lab offers an introduction to the S-plus
statistical computing
environment, including features such as customized graphics, language
extensions, and interface with other languages.
Is a co-requisite for Statistics 230b, Statistics 312a
and Statistics 361a and is recommended for those
taking Statistics
242b.

* The first five weeks of the course will present a rapid
introduction to the main features of Splus, which students from other
Statistics courses are welcome to audit.*

[SYLLABUS]

Time: Fri., 2:30-5:00 at Stat Lab, 140 Prospect

**Statistics 230b (66230), Introductory Data Analysis **
* Cross-listing: Statistics 530b, PLSC 530b *

Instructor: Mr. N. Hengartner.

Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. Some sessions are used to demonstrate techniques on the computers. Concurrent with Statistics 200Lb; after or concurrent with Statistics 123a or Psychology 200a or b or equivalent.

[SYLLABUS & CLASS INFORMATION]

Time: Tue., Thu., 2:30-3:45

**Statistics 241a (66241), Probability Theory **
* Cross-listing: Statistics/Mathematics 541a *

Instructor: Mr. J. Hartigan.

A first course in probability theory: probability spaces, random variables, expectations and probabilities, conditional probability, independence, some discrete and continuous distributions, central limit theorem, Markov chains, probabilistic modeling. After or concurrent with Mathematics 120a or b or equivalents.

[SYLLABUS]

Time: Mon., Wed., Fri., 9:30-10:20

**Statistics 242b (66242), Theory of Statistics **
* Cross-listing: Statistics 542b, Mathematics 242b *

Instructor: Mr. M. Wegkamp.

Principles of statistical analysis: maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. After Statistics 241a; after or concurrent with Mathematics 222; Statistics 200Lb recommended.

Time: Mon., Wed., Fri., 9:30-10:20

**Statistics 251b (66251), Stochastic Processes **
* Cross-listing: Statistics 551b *

Instructor: Mr. N. Hengartner.

A study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion and diffusions. Introduction to certain modern techniques in probability such as coupling and large deviations. Applications to image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, genetics and evolution. After Statistics 241a or equivalent.

Time: Mon., Wed., 2:30-3:45

**Statistics 312a (66312), Linear Models **
* Cross-listing: Statistics 612a *

Instructor: Mr. M. Wegkamp.

The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms (with particular reference to S-plus); alternatives to least squares. Generalized linear models. After Statistics 242b and Mathematics 222 or equivalents. Statistics 200Lb is a prerequisite.

[SYLLABUS]

Time: Tue., Thur., 10:30-11:20

**Statistics 361a (66361), Data Analysis **
* Cross-listing: Statistics 661a*

Instructor: Mr. J. Chang.

By analyzing data sets using the S-plus statistical computing language, a selection of Statistical topics are studied: linear and non-linear models, maximum likelihood, resampling methods, curve estimation, model selection, classification and clustering. Weekly sessions will be held in the Social Sciences Statistical Laboratory. After Statistics 242b or equivalent. Statistics 200Lb is a prerequisite.

[SYLLABUS]

Time: Mon., Wed., 2:20-3:45

**Statistics 364b (66364), Introduction to Information Theory **
* Cross-listing: Statistics 664b*

[Alternate years, next offered Spring 1998]

**Statistics 600b (66600), Advanced Probability **
* Cross-listing: Statistics 330b *

Instructor: Mr. A. Barron.

Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis is assumed.

Time: Tue., Thur., 2:30-3:45

**Statistics 606b (66606), Markov Chains **

Instructor: Mr. J. Chang

Random walks, mixing times, stopping rules, threshold phenomena.
Harris chains and general state spaces. Renewal theory. Applications
to simulation and optimization. Other topics as time permits:
diffusions, potential theory, large deviations, hidden Markov models.
Prerequiste:
Statistics 600b or consent of instructor.

Time: Times to be arranged at organizational meeting

**Statistics 610a (66610), Statistical Inference **

Instructor: Mr. J. Chang

A systematic development of the mathematical theory of statistical
inference covering methods of estimation, hypothesis testing, and
confidence intervals. An introduction to statistical decision
theory. Undergraduate probability at the level of
Statistics 241a assumed.

Time: Tue., Thur., 10:30-11:45

**Statistics 618b (66618), Asymptotic Theory **

Instructor: Mr. D. Pollard and Mr. M. Wegkamp.

A careful introduction to asymptotic methods in mathematical statistics.
Topics include: consistency and asymptotic distributions, contiguity,
efficiency,
likelihood ratio theory, and (if time permits) Le Cam's theory for
convergence of experiments.
After Statistics 600b and Statistics 610b

Time: Times to be arranged at organizational meeting

**Statistics 625a (66625), Statistical Case Studies **

Instructor: Mr. D. Pollard.

Thorough analysis of complex data sets using S-plus, with emphasis on
the balance between graphical techniques and formal inferential
procedures. Interpretations and use of Census data and mapping software.
The course will
focus on the analysis of a huge data set related to a consulting problem on
jury selection.

[SYLLABUS]

Time: Tuesday 3:00-5:00

**Statistics 626b (66626), Practical Work **

Instructor: Staff.

Individual one-semester projects, with students working on studies
outside the Department, under the guidance of a statistician.

Time: Times to be arranged at organizational meeting.

**Statistics 685a (66685), Classification **

Instructor: Mr. J. Hartigan.

Statistical methods of identifying classes, types and clusters, uses of
classification in prediction and inference. Recognition, k-means, minimum
spanning trees, hierarchical clustering algorithms, density estimation;
model estimation. Mixture models, product partition models, excess mass
models change point models, block clustering models, percolation.
Applications to reticulate evolution, mammalian teeth, parliamentary
voting, subtypes of schizophrenia, and foundations of probability.

Time: Times to be arranged at organizational meeting

**Statistics 690a (66690), Introduction to Research**

Instructor: Mr. A. Barron

Formation and development of research topics. Students will read and
review literature and give oral presentations. Discussion of methods to
address open problems in statistics.

Time: Times to be arranged at organizational meeting.

**Statistics 700, Departmental Seminar **

Important activity for all members of the department. Either at
24 Hillhouse Avenue or at EPH. See
weekly seminar announcements.

Time: Monday 4:15-

Instructor: Mr. E. Tufte

Techniques for the visual display of quantitative information, including multivariate graphics. Examination of many examples of data graphics in science, social science and journalism. Development of empirical measures of graphical performance. Also the use of tables and words to convey statistical data.

Time: Tuesday, 3:30-5:20 at 124 Prospect Room 102