Statistics 101-106, (66101,66106), Introduction to Statistics (FALL)
Statistics 103a - Soc 119a -Soc. 580a/119a - Introduction to Statistics:
Social
Sciences. (66103)
Statistics 104a - Psychology 201a Introduction to Statistics:
Psychology. (66104)
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.
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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.
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.
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.
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.
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.
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)
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, 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.
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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.
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.
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
Instructor:
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.
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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.
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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.
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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.
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.
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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