Yale Statistics 1998-99 Course List

Yale University
Department of Statistics

Yale Statistics Courses

Course List for 1998-99

Primarily undergraduate courses

Director of Undergraduate Studies: Nicolas Hengartner

Statistics 101-106, (66101,66106), Introduction to Statistics (FALL)
Cross-listing: Statistics 501a-506a

Instructor: Mr. D. Pollard. T Th 1:00 pm - 2:15 pm
Each of these courses gives a basic introduction to statistics, requiring no mathematics beyond high school algebra. Topics include numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The Tuesday lecture, which introduces general concepts and methods of statistics, is attended by all students in Statistics 101-106 together. The course separates for Thursday lectures, which develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence and only one may be taken for credit. They do not count toward the natural sciences requirement.

Statistics 101aG - Biology 150aG (66101) Introduction to Statistics: Life Sciences.
Instructor: Mr. David Pollard/ Mr. Junhyong Kim.
Statistical and probabilistic analysis of biological problems presented with a unified foundation in basic statistical theory. The problems are drawn from genetics, ecology, epidemiology, and bioinformatics.

Statistics 102aG - EP & E 203a - Political Science 425a (66102) Introduction to Statistics: Social Sciences.
Instructor: Mr. David Pollard/Mr. Donald Green.
Statistical analysis of social science problems, primarily drawn from political science and sociology, presented with a unified foundation in basic statistical theory.

Statistics 103a - Soc 119a -Soc. 580a/119a - Introduction to Statistics: Social Sciences. (66103)
Instructor: Mr. David Pollard/Mr. Dalton Conley.
An introduction to statistical methods of sociology. Topics include descriptive statistics and analysis of social data with one or more variables. Through in-class analysis of sociological journal articles, students will also become statistically literate with respect to quantitative research.

Statistics 104a - Psychology 201a Introduction to Statistics: Psychology. (66104)
Instructor: Mr. David Pollard/ Ms. Susan Brandon.
Statistical and probabilistic analysis of psychological problems presented with a unified foundation in basic statistical theory. The problems are drawn from studies of sensory processing and perceptions, development, learning, and psychopathology.

Statistics 105a - F & ES 205aG (66105) Introduction to Statistics: Environmental Sciences.
Instructor: Mr. David Pollard/Mr. Timothy Gregoire.
An introduction to probability and statistics with emphasis on applications to forestry and environmental sciences, presented with a unified foundation in basic statistical theory.

Statistics 106a - Introduction to Statistics: Data Analysis. (66106)
Instructor: Mr. David Pollard/Mr. John Hartigan.
An introduction to probability and statistics with emphasis on data analysis.

Probability and Statistics for Economics, Earth Sciences and Engineering (SPRING)
Economics 161b, Geology & Geophysics 359b/559b, Engineering and Applied Science 496b
[Unofficial numbers: Statistics 211-213]

Cross-listing: Statistics 510b
Instructors: Mr. A. Barron, Mr. P. Belhemuer, Mr. J. Lees, Mr. J. Rust.

Each of these three courses introduce elements of probability and statistics, along with some aspects of data analysis. Separately the courses provide applications to particular fields of study. Each course is taught jointly by two instructors, one from the Statistics department and the other in the relevant fields of study. The general lectures given by Andrew Barron are held on Mondays and Wednesdays prior to the Spring Break (Jan 11 through Mar 3, covering basic data analysis, probability and inference) and on Mondays only after the break (Mar 22 through Apr 19, covering basics of multivariate regression, random processes, and time series). These general lectures are attended by all students in the three classes together. The field specific lectures given by Professors Rust, Lees and Belhumeur are held on Fridays only before the break (Jan 15 through Mar 5) and Wednesdays and Fridays after the break (Mar 24 through Apr 23). Field specific descriptions are given below.

Economics 161b (26161): Econometrics and Data Analysis (Barron/Rust)
A course that aims to provide students with sufficient knowledge of theory and practice of statistics and econometrics to enable them to be effective consumers and producers of empirical research in the social sciences. Simple regression, distribution theory, estimation and inference, multiple regression model and extensions. Computer use required; no prior experience assumed. After two terms of introductory economics and completion of the mathematics requirement for the major.

Geology & Geophysics 359b/559b (33359): Data Analysis in the Earth Sciences (Barron/Lees)
Quantitative analysis of data from the earth sciences, including geophysics, geology, geochemistry, and paleontology. Strong emphasis placed on applications and real world examples of statistical techniques.

Engineering and Applied Science 496b: Probability and Stochastic Processes (Barron/Belhemuer)
This course aims to provide students with the fundamentals of probability, statistics, and random processes for engineering. The course will be filled with real examples taken from communications, signal and image processing, estimation, pattern recognition, computer vision, control theory, and speech recognition.

Statistics 230b (66230), Introductory Data Analysis (SPRING) Cancelled for spring 1999
Cross-listing: Statistics 530a, PLSC 530b

Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. Techniques are demonstrated on the computer. After or concurrent with Statistics 101a.

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

Instructor: Mr. N. Hengartner. MWF 9:30 am - 10:20 am
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.

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

Instructor: Mr. M. Wegkamp. MWF 9:30 - 10:20
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 251b (66251), Stochastic Processes (SPRING)
Cross-listing: Statistics 551b

Instructor: Mr. D. Pollard. M W 1 - 2:15
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.

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

Instructor: Mr. M. Wegkamp. T TH 9:00 am - 10:15 am
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 361b (66361), Data Analysis (SPRING)
Cross-listing: Statistics 661b
Instructor: Mr. J. Hartigan. M W 2:30 - 3:45 pm
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 242 or equivalent.

Statistics 533b, Census Data Postponed to Fall 1999
Cross-listing: Statistics 233b

Instructor: Mr. D. Pollard
An introduction to some of the many uses for data collected by the Bureau of the Census. The decennial census: printed tables, summary tape files, microdata (PUMS), census geography, the TIGER database. Maps and geocoding. Patterns across time. How accurate is a sample? Estimation and inference from census data. Undercount and the possibility of adjustment--what the Supreme Court has to say about statistics. What is race? What is Hispanic? What does the Bureau do for the other nine years? Why all the fuss over Census 2000? Students should bring to the course a basic understanding of statistics (sampling, means and variances, normal approximations) and the ability to work with some statistical computer package, such as Splus. The course will focus on data for New Haven.

Primarily graduate courses

Director of Graduate Studies: Andrew Barron
Acting Director of Graduate Studies (FALL): John Hartigan

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

Instructor: Mr. D. Pollard. T TH 2:30 - 3:45
Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis is assumed.

Statistics 610a (66610), Statistical Inference (FALL)
Instructor: Mr. N. Hengartner.
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.

Statistics 625a (66625), Statistical Case Studies (FALL)
Instructor: Mr. J. Hartigan.
We will study large data sets on second hand smoke, reticulate evolution, bloc voting, NCAA Academic Thresholds, Connecticut Educational Standards - and other fun things.

Statistics 626b (66626), Practical Work (SPRING)
Instructor: Mr. J. Hartigan.
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 653a (66653), Bayes Methods (FALL)
Instructor: Mr. J. Hartigan.
Axioms and interpretations of probability. Construction of probability distributions. Optimality of Bayes procedures. Martingales. Asymptotics. Markov Sampling. Robustness against violations in the assumed distributions. Choice among models.
Time: Times to be arranged at organizational meeting.

Statistics 665b (66665), Introduction to Function Estimation (SPRING)
Cross-listing: Statistics 365b

Instructor: Mr. N. Hengartner. M W 11:30 - 12:45
A practical introduction to modern curve estimation techniques, such as non-linear regression, regression splines, series estimators, local regression smoothers and neural networks, with discussion of boundary effects, model and bandwidth selection, goodness of fit and confidence intervals/bands. Further topics include estimation under shape restriction, pattern recognition, inverse problems, hazard estimation and density estimation.

Statistics 667a (66667), Pattern Recognition (FALL)
Instructor: Mr. M. Wegkamp
Pattern recognition, or discrimination, is about guessing or predicting the unknown nature of an observation, a discrete quantity such as black or white, one or zero, sick or healthy, real or fake according to Devroye, Gyorfi and Lugosi in "A probabilistic theory of pattern recognition", Springer-Verlag (1996). Some of the topics I intend to discuss are issues like consistency, error probabilities and universality of many popular classification rules. This course is aimed at computer scientists, engineers, mathematicians, statisticians and anyone who is interested in the theoretical aspects of pattern recognition. We will follow the aforementioned book by Devroye, Gyorfi and Lugosi.
Time: Times to be arranged at organizational meeting.

Statistics 668b (66668), Information & Probability (SPRING)
Instructor: Andrew Barron
Fundamental identities of Information Theory, including a chain rule, a pythagorean identity for relative entropy (Kullback divergence) and identities linking entropy and Fisher information, are used to prove and extend basic limit theorems of Probability Theory, including the central limit theorem, martingale convergence, large deviations, and convergence of the distributions of Markov chains. New research topics are introduced. Prerequisite or Corequisite: Statistics 600.
Time: Times to be arranged at organizational meeting.

Stat 687a (66687), Evolutionary Trees postponed
Instructors: Joseph Chang and Junhyong Kim
Methods of phylogeny reconstruction and their statistical and algorithmic properties. Sequence alignment, Markov models, parsimony,distance methods, maximum likelihood, reliability of estimated trees, large-scale phylogenies, site-to-site rate variation and dependence. Issues of choosing characters, combining data sets, and comparing trees. Applications to bioinformatics, epidemiology, ecology, molecular biology, and development. Familiarity with computers and with probability and statistics at the level of Statistics 241 and 242 will be assumed.

Statistics 700, Departmental Seminar (24 Hillhouse Avenue)
Important activity for all members of the department. See weekly seminar announcements.
Time: Monday 4:15-

Revision: 05 JAN 1999