STAT 103a / SOCY 119a, Introduction to
Statistics: Social Sciences.
Instructor: Mr. Jonathan Reuning-Scherer
An introduction to probability and statistics, with
emphasis on experimental design and data analysis. Survey of many of the
great experiments in social science. Topics include obedience to
authority, conformity to social pressure, and susceptibility to perceptual
distortions.
STAT 104a / PSYC 201a, Introduction to Statistics:
Psychology.
Instructor: Ms. Jonna
Kwiatkowski
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 perception, development, learning, and psychopathology.
STAT 105a, Introduction to Statistics:
Medicine.
Instructor: Mr. Marek
Chawarski
Statistical methods relied upon
in medicine and medical research. Practice in reading medical literature
competently and critically, as well as practical experience performing
statistical analysis of medical data.
STAT 106a, Introduction to Statistics: Data
Analysis.
Instructor: Mr. John
Hartigan
An introduction to probability and
statistics with emphasis on data analysis.
STAT 230b, Introductory
Data Analysis (SPRING) Cross-listing:
Statistics 530a, PLSC 530b
Instructor: Mr.
John Emerson
Time: Mon, Wed 1:00 - 2:15,
2:30 - 3:45
Place: PR 140
Statlab
Survey of statistical methods: plots,
transformations, regression, analysis of variance, clustering, principal
components, contingency tables, and time series analysis. Uses SPLUS and Web
data sources. After or concurrent with Statistics
101-106.
STAT 238a, Probability and
Statistics for Scientists (FALL)
Cross-listing: Statistics 538a
Instructor: Mr. Joseph Chang
Time: Mon, Wed, Fri 2:30-3:20
Place: ML 211
Fundamental
principles and techniques that help scientists think probabilistically, develop
statistical models, and analyze data.
Essentials
of probability: conditional probability, random variables, distributions, law of
large numbers, central limit theorem, Markov chains. Statistical inference
with emphasis on the Bayesian approach: parameter estimation, likelihood,
prior and posterior distributions, Bayesian inference using Markov chain Monte
Carlo. Introduction to regression and linear models. Computers are
used throughout for calculations, simulations, and analysis of data.
After MATH 118a or b or 120a or b.
Some
acquaintance with matrix algebra and computing assumed.
STAT 241a, Probability
Theory with Applications (FALL)
Cross-listing: Statistics 541a
Instructor: Mr. Hannes Leeb
Time: Mon, Wed, Fri 9:30 - 10:20
Place: WLH 208
A first course
in probability theory: probability spaces, random variables, expectations and
probabilities, conditional probability, independence, some discrete and
continuous distributions, central limit theorem, law of large numbers. After or
concurrent with Mathematics 120a or b or equivalents.
STAT 242b, Theory of
Statistics (SPRING)
Cross-listing:
Statistics 542b, Mathematics 242b
Instructor: Mr. Hannes Leeb
Time: Mon, Wed, Fri 9:30 - 10:20
Place: BCT 102
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.
STAT 251b, Stochastic
Processes (SPRING)
Cross-listing:
Statistics 551b
Instructor: Mr. David
Pollard
Time: Mon, Wed 1 - 2:15
Place: BCT 102
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.
[MORE COURSE INFORMATION]
STAT 312a, Linear Models
(FALL)
Cross-listing: Statistics
612a
Instructor: Mr. Hani Doss
Time: Tues, Thurs 9:00-10:15
Place: 24 Hillhouse Avenue, Room 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.
[MORE COURSE INFORMATION]
STAT 361a, Data Analysis
(FALL)
Cross-listing: Statistics
661a
Instructor: Mr. John
Hartigan
Time: Mon, Wed 2:30 -
3:45
Place: Statlab - 140
Prospect
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. After
Statistics
242 and Mathematics 222b or 225a or b, or equivalents.
STAT 364b, Information
Theory (SPRING)
Cross-listing: Statistics
664b
Instructor: Mr. Andrew
Barron
Time: Tue, Thu
9:00 - 10:15
Place: 24 Hillhouse Avenue,
Room 107
Foundations of information theory in
mathematical communications, statistical inference, statistical mechanics,
probability, and algorithmic complexity. Quantities of information and their
properties: entropy, conditional entropy, divergence, redundancy, mutual
information, channel capacity. Basic theorems of data compression, data
summarization, and channel coding. Applications in statistics and finance. After
Statistics 241.
[MORE COURSE INFORMATION]
STAT 365b, Data Mining and Machine
Learning (SPRING)
Cross-listing: Statistics 665b
Instructor: Mr. Hannes Leeb
Time: Mon, Wed 11:30 - 12:45
Place: 24 Hillhouse, Room 107
Techniques for data mining and machine learning are covered from
both a statistical and a computational perspective, including support vector
machines, bagging, boosting, neural networks, and other nonlinear and
nonparametric regression methods. The course will give the basic ideas and
intuition behind these methods, a more formal understanding of how and why they
work, and opportunities to experiment with machine learning algorithms and apply
them to data. After STAT 242b.
[STAT 374a, Analysis of Spatial and
Time Series Data (FALL)] Not offered 2003.
Cross-listing: Statistics 674a
AM490b, Applied Math
Senior Seminar and Project (SPRING)
Cross-listing:
Instructor:
Mr. Andrew Barron
Time: Wed 3:30 -
5:20
Place: 24 Hillhouse, Room
107
Under the supervision of a member of the
faculty, each student works on an independent project. Students
participate in seminar meetings at which they speak on the progress of their
projects. Some meetings are devoted to talks by visiting applied
mathematicians.
Primarily graduate courses
Director of Graduate Studies:
Professor John Hartigan.
STAT 600b, Advanced
Probability (SPRING)
Cross-listing:
Statistics 330b
Instructor: Mr. David
Pollard
Time: Tues, Thurs 2:30 -
3:45
Place: LUCE 202
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]
STAT 603a, Stochastic
Calculus (FALL)
Instructor: Mr.
Joseph Chang
Time: Tues, Thurs
10:30-11:45
Place: 24 Hillhouse, Room
107
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,
are reviewed. After Statistics 600.
STAT 606a, Monte Carlo
Markov Chains (FALL)
Instructor: Mr.
Hani Doss
Time: Tues, Thurs 2:30 -
3:45
Place: 24 Hillhouse, Room
107
Markov chain Monte Carlo is a simulation
method for estimating distributions and expectations that are analytically
intractable. This course will discuss theory and applications of the
method. Topics to be covered include: the Metropolis-Hastings algorithm
and the Gibbs sampler; applications in survival analysis, hierarchical models
and nonparametric Bayes problems; convergence theorems; convergence
diagnostics.
[MORE COURSE INFORMATION]
STAT 610a, Statistical
Inference (FALL)
Instructor: Mr. Andrew
Barron
Time: Mon, Wed 1:00 pm - 2:15
pm
Place: 24 Hillhouse Avenue, Room
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.
[MORE COURSE
INFORMATION]
STAT 625a, Statistical Case
Studies (FALL)
Instructor: Mr. John
Emerson.
Time: Wed 9:30 - 12:00
Place: 124 Prospect, Room B-13 (Brewster
Hall)
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.
[MORE COURSE INFORMATION]
STAT 626b, Practical Work
(SPRING)
Instructor: Mr. John Emerson/Staff.
Time: W 9:00 - 10:15, F 9:00 - 12:00
Place: W - 24 HLH Rm 107, F - 124 Prospect Rm B-13
Individual one-semester projects, with students working on studies outside the Department, under the guidance of a statistician.
STAT 627b, Statistical
Consulting (SPRING)
Instructor: Mr. John Emerson/Staff.
Time: W, F 9:00 - 10:15
Place: W - 24 HLH Rm 107, F - 124 Prospect Rm B-13
Statistical consulting and collaborative research projects usually require the statistician to explore new topics outside their area of expertise. This course exposes students to real problems, requiring them to draw on their
expertise in probability, statistics, and data analysis. Students complete the course with individual consulting projects, under the guidance of a
statistician.
The course will meet twice a week; classes and assignments will center around discussing and working on problems posed by researchers from across campus.
Prior coursework in statistics is assumed: the equivalent of at least two courses in statistics or its applications, experience with methods of data analysis,
or with permission from the instructor.
STAT 645b, Statistical
Methods in Genetics and Bioinformatics (SPRING)
Instructor: Mr. Hongyu Zhao
Time: Tues, Thurs 10:30 - 11:45 am
Place: Room 126, LEPH (60 College Street)
Stochastic modeling and
statistical methods applied to problems such as mapping quantitative trait loci,
analyzing gene expression data, sequence alignment, and reconstructing
evolutionary trees. Statistical methods include maximum likelihood, Bayesian
inference, Monte Carlo Markov chains, and some methods of classification and
clustering. Models introduced include variance components, hidden Markov models,
Bayesian networks, and coalescent. Recommended background: Stat 541, Stat 542.
Prior knowledge of biology is not required.
STAT 660b, Multivariate Statistics for Social Sciences (SPRING)
Instructor: Mr. Jonathan Reuning-Scherer
Time:; Tues, Thur 1:00 pm - 2:15 pm
Place: Tues WLH 202, Thur Statlab 140 Prospect
A practical introduction to the analysis of multivariate data as applied to
examples from the social sciences. Topics to include multivariate analysis of variance (MANOVA),
principle components analysis, cluster analysis (hierarchical clustering, k-means), canonical correlation,
multidimensional scaling, factor analysis, discriminant analysis, and structural equations modeling.
Emphasis is placed on practical application of multivariate techniques to a variety of examples in the
social sciences. There are regular homework assignments and a final project. Regular use of some
statistical software package (students may choose among SAS, SPSS, and MINITAB). A complete
syllabus will be available on the classes server.
STAT 678a, Portfolio
Estimation for Compounding Wealth (FALL)
Instructor: Mr. Andrew Barron
Time: Wed 4:00-6:30 pm
Place: 24 Hillhouse Avenue, Room 107
A study of distributional properties of compounded wealth in
repeated gambling and in stock market investment. Wealth concentration inequalities. Strategies of highest concentrated
wealth. Normal theory for log-wealth. Relationship to maximum likelihood theory in statistics and to the asymptotic
equipartition property in physics and information theory. Greedy strategies. Universal portfolios and their
relationship to Bayes methodology. The ratio of idealized wealth (best with hindsight) to actual wealth and the properties of
this ratio, both for stochastic stock price sequences and its minimax behavior for arbitrary price sequences. Fast algorithms
for universal portfolios.
[MORE COURSE
INFORMATION]
STAT 683b, Asymptotics
(SPRING)
Instructor: Mr. John
Hartigan
Time: T, TH 2:30 - 3:45
Place: 24 Hillhouse Avenue, Room 107
Probabilistic limits, asymptotic normality, orders of magnitude, cumulants, edgeworth expansions,
asymptotic properties of bayes procedures, asymptotic equivalence of maximum likelihood estimation
and bayes procedures, asymptotic equivalence of Akaike model selection and bayes procedures,
asymptotic admissibility.
STAT 695a, Internship in
Statistical Research (1 credit) (FALL)
Instructor: DGS
The Internship
is designed to give students an opportunity to gain practical exposure to
problems in the analysis of statistical data, as part of a research group within
industries such as: medical and pharmaceutical research, financial, information
technologies, telecommunications, public policy, and others. The
Internship experience often serves as a basis for the Ph.D. dissertation.
Students will work with the Director of Graduate Studies and other faculty
advisors to select suitable placements.
Students will submit a one-page description of their
Internship plans to the DGS by May 1st, which will be evaluated by the DGS and
other faculty advisors by May 15th. Upon completion of the Internship,
students shall submit a written report of their work to the DGS, no later than
October 1st. The Internship will be graded on a Satisfactory/
Unsatisfactory basis, and will be based on the student's written report and an
oral presentation.
This course is an elective
requirement for the Ph.D. degree.
STAT 700, Departmental Seminar
Time: Monday
4:15 pm - 5:30 pm
Important activity for all members of the department.
24 Hillhouse Avenue. See weekly seminar
announcements.
Course lists for prior years may be found here.
Please note these older pages are not being updated, and as such, some of the links may no longer be working.
Revision: 6 Jan 2004
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