| course | number | instructor | level | time |
|---|---|---|---|---|
| Introduction to Statistics | 101-105a | Chang et al | intro, no prereqs | T,Th 1:00-2:15 |
| Introductory Data Analysis | 230/530b | Hartigan | intro | M,W 1:00-2:15 and 2:30-3:45 |
| Probability Theory | 241/541a | Pollard | intro, with calculus | M,W,F 9:30-10:20 |
| Theory of Statistics | 242/542b | Hengartner | intro, with calculus | M,W,F 9:30-10:20 |
| Stochastic Processes | 251/551b | Wegkamp | intermediate | M,W 1:00-2:15 |
| Analysis of spatial and time series data | 374/674 | Hengartner | T,Th 1:00-2:15 | |
| Statistical genetics and bioinformatics | 645 | Chang | M,W 11:00-12:15 | |
| Applied Math senior seminar | AM490b | Barron | W 3:30-5:20 | |
| [ |
[364] | intermediate | alternate years | |
| Linear Models | 312/612a | Wegkamp | intermediate | T,Th 9:00-10:15 |
| Data Analysis | 361/661b | Hengartner | intermediate | M,W 2:30-3:45 |
| Statistical Case Studies | 625a | Hartigan | intermediate grad | M,W 2:30-3:45 |
| Practical Work | 626b | staff | adv. grad | |
| Statistical Inference | 610a | Barron | intro grad | W,F 1:10-2:25 |
| Advanced Inference | 619b | Barron | advanced grad | T,Th 10:30-11:45
first meeting Jan 11th |
| Advanced Probability | 330/600b | Wegkamp | adv. undergrad/
intermediate grad |
T,Th 2:30-3:45 |
| Inequalities in probability and statistics | 607a | Pollard | intermediate grad | T,Th 3:45-5:00 |
| 603a | adv. grad | alternate years | ||
| Classification | 685b | Hartigan | adv. grad | T, Th 2:30 - 3:45
first meeting Jan 11th |
Statistics 101-105, Introduction to
Statistics (FALL)
Cross-listing: Statistics 501a-505a
Instructor: Mr. J. Chang and faculty from other departments.
Time: Tues, Thurs 1:00 pm - 2:15 pm
Place: OML 202 (Tuesday)
A basic introduction to statistics, including 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 (sections), 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. No prerequisites
beyond high school algebra.
[Statistics 103a - Soc 119a -Soc. 580a/119a - Introduction to Statistics: Social Sciences.] Not offered 2000
Statistics 104a - Psychology 201a Introduction to Statistics: Psychology.
Instructor: Mr. Joseph Chang/Mr. Tom Brown.
Place: OML 202 (Tuesday), LUCE 202 (Thursday)
[MORE COURSE INFORMATION]
Statistics 230b, Introductory Data
Analysis (SPRING)
Cross-listing: Statistics 530a, PLSC 530b
Instructor: Mr. J. Hartigan
Time: Mon, Wed 1:00 - 2:15 and 2:30 - 3:45
Place: Room 100, (Stat Lab) 140 Prospect Street
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 Statistics 101-105.
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Statistics 241a, Probability Theory
(FALL)
Cross-listing: Statistics/Mathematics 541a
Instructor: Mr. D. Pollard.
Time: Mon, Wed, Fri 9:30 - 10:20
Place: ML 104
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.
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Statistics 242b, Theory of Statistics
(SPRING)
Cross-listing: Statistics 542b, Mathematics 242b
Instructor: Mr. N. Hengartner.
Time: Mon, Wed, Fri 9:30 - 10:20
Place: 200 LOM
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.
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COURSE INFORMATION]
Statistics 251b, Stochastic Processes
(SPRING)
Cross-listing: Statistics 551b
Instructor: Mr. M. Wegkamp.
Time: Mon, Wed 1 - 2:15
Place: 102 BCT
Introduction to the study of random processes, including Markov chains,
Markov random fields, martingales, random walks, Brownian motion and diffusions.
Tecniques 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.
After Statistics 241a or equivalent.
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COURSE INFORMATION]
Statistics 312a, Linear Models (FALL)
Cross-listing: Statistics 612a
Instructor: Mr. M. Wegkamp.
Time: Tues, Thurs 9:00-10:15
Place: 24 HH Rm. 107
The geometry of least squares; distribution theory for normal errors;
regression, analysis of variance, and designed experiments; numerical algorithms
(with particular reference to Splus); alternatives to least squares. Generalized
linear models. After
Statistics 242b and Mathematics
222 or equivalents.
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COURSE INFORMATION]
Statistics 361b, Data Analysis (SPRING)
Cross-listing: Statistics 661b
Instructor: Mr. N. Hengartner.
Time: Mon, Wed 2:30 - 3:45
Place: 24 Hillhouse, Room 107
Through analysis of data sets using the Splus statistical computing
language, study of a selection of statistical topics such as linear and
nonlinear 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 and Mathematics
222b or 225a or b, or equivalents.
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COURSE INFORMATION]
[Statistics 364b, Information Theory]
Cross-listing: Statistics 664b
Statistics 374a, Analysis of spatial
and time series data
Cross-listing: Statistics 674a
Instructor: Mr. N. Hengartner.
Time: Tues, Thurs 1:00-2:15
Place: 24 HH Room 107
Study of statistical models that are useful for describing data collected
over space or time. Models include frequency domain and time domain analysis
of time series; state space models and Kalman filters; point processes;
Gibbs processes and random fields.
[MORE
COURSE INFORMATION]
AM490, Applied Math senior seminar
Cross-listing:
Instructor: Mr. A. Barron.
Time: Wed 3:30 - 5:20
Place:
Statistics 600b, Advanced Probability
(SPRING)
Cross-listing: Statistics 330b
Instructor: Mr. M. Wegkamp
Time: Tues, Thurs 2:30 - 3:45
Place: 102 BCT
Measure theoretic probability, conditioning, laws of large numbers,
convergence in distribution, characteristic functions, central limit theorems,
martingales. Some knowledge of real analysis is assumed.
[MORE
COURSE INFORMATION]
[Statistics 603a, Stochastic Calculus
(SPRING) ]
Instructor: NEXT TAUGHT IN 2001-2002
Time:
Martingales in discrete and continuous time, Brownian Motion,
Sample path properties, predictable processes, stochastic integrals with
respect to Brownian motion and semimartingales, stochastic differential
equations. Applications mostly to counting processes and finance. Knowledge
of measure-theoretic probability at the level of Statistics 600 is a prerequisite
for the course, although some key concepts, such as conditioning, will
be reviewed. After: Statistics 600.
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COURSE INFORMATION]
Statistics 607a, Inequalities in probability
and statistics (FALL)
Instructor: Mr. D. Pollard.
Time: Tues, Thurs 3:45-5:00
Place: 24 HH Rm. 107
A study of a variety of useful inequalities. The course will be broken
into independent segments, each treating a specific method and an illustrative
application. Topics include: tail bounds for normal distributions; convexity
methods; Bennett and Hoeffding inequalities for independent, bounded summands;
Poisson-Binomial trials; martingale methods; mixing inequalities; maximal
inequalities based on entropy calculations; Tusnady's inequality and strong
approximation; Hellinger, total variation, and divergence distances between
measures; concentration inequalities; isoperimetric inequalities. Acquaintance
with probability at the 600 level helpful for some segments.
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COURSE INFORMATION]
Statistics 610a, Statistical Inference
(FALL)
Instructor: Mr. A. Barron.
Time: Wed, Fri Wed 1:10 - 2:25
Place: 24 HH Rm. 107
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.
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COURSE INFORMATION]
Statistics 619b, Advanced Inference
(SPRING)
Instructor: Mr. A. Barron.
Time: Tues, Thurs 10:30 - 11:45
Place: 24 HH Rm. 107
Topics of mathematical statistics including predictive distributions,
exchangeability and DeFinetti's representation theorem, finite and infinite
parameterizations, axiomatic decision theory, large sample properties of
Bayes procedures, Heirarchical models, and sequential analysis. Textbook:
Theory of Statistics, by Mark Schervish. After Statistics
610a, after or concurrent with Statisics 600b.
Statistics 625a, Statistical Case
Studies (FALL)
Instructor: Mr. J. Hartigan.
Time: Mon, Wed 2:30-3:45
Place: 24 HH Rm. 107
Thorough study of some large data sets on such topics as second-hand
smoking, crashes in small cars, reticulate evolution, bloc voting, and
Connecticut educational standards.
Statistics 626b, Practical Work
(SPRING)
Instructor: Mr. N. Hengartner
Individual one-semester projects, with students working on studies
outside the Department, under the guidance of a statistician.
.
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COURSE INFORMATION]
Statistics 645a, Statistical Genetics
and Bioinformatics (FALL)
Instructor: Mr. J. Chang.
Time: Mon, Wed 11:00-12:15
Place: 24 HH Rm. 107
Types of available genetic data and types of questions they can address.
Fundamentals of population genetics. Locating genes for discrete and continuous
traits. Linkage disequilibrium, association mapping, and pedigree analysis.
Database searching, sequence alignment and hidden Markov models. Reconstruction
of evolutionary trees. Functional genomics and analysis of gene expression
data. Knowledge of basic mathematics (calculus and linear algebra), probability
and statistics at the STAT 541-542 level assumed.
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COURSE INFORMATION]
Statistics 685b (66685), Classification
(SPRING)
Instructor: Mr. J. Hartigan.
Time: Tues, Thurs 2:30 - 3:45
Place: 124 Prospect Rm. B-13
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.
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COURSE INFORMATION]
Statistics 700, Departmental Seminar
Time: Monday 4:15-
Important activity for all members of the department. 24 Hillhouse
Avenue. See
weekly seminar announcements.