course | number | instructor | level | time |
---|---|---|---|---|
Introduction to Statistics | 101-105a | Chang et al | intro, no prereqs | T, Th 1-2:15 |
Census Data | 233/533a | Pollard | intro | T, Th 2:30-3:45 |
Probability Theory | 241/541a | Wegkamp | intro, with calculus | MWF 9:30-10:20 |
Linear Models | 312/612a | Barbe | intermediate | T, Th 9-10:15 |
Approximation of probability distributions |
608a | Barbe | adv. grad | T, Th 12-1 |
Statistical Inference | 610a | Wegkamp | intro grad | M, W 1 - 2:30 |
Statistical Case Studies | 625a | Pollard | intermediate grad | W 2:30 - 4:30 |
Introduction to Research | 690a | Barron | adv. grad | T, Th 3:45 - 5:00 |
| ||||
Introductory Data Analysis | 230/530b | Hartigan | intro | MW 2:30-3:45 |
Theory of Statistics | 242/542b | Hengartner | intro, with calculus | MWF 9:30-10:20 |
Stochastic Processes | 251/551b | Chang | intermediate | MW 1-2:15 |
Advanced Probability | 330/600b | Pollard | adv. undergrad/ intermediate grad |
T, Th 2:30-3:45 |
Information Theory | 364b | Barron | intermediate | T, Th 9-10:15 |
Data Analysis | 361/661b | Barbe | intermediate | MW 2:30-3:45 |
Stochastic Calculus | 603b | Chang | adv. grad | ? |
Foundations of Statistics | 605b | Hartigan | adv. grad | ? |
Practical Work | 626b | staff | adv. grad | ? |
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.
[MORE COURSE INFORMATION]
[Statistics 103a - Soc 119a -Soc. 580a/119a - Introduction to Statistics: Social Sciences.] Not offered 1999
[Statistics 104a - Psychology 201a Introduction to Statistics: Psychology.] Not offered 1999.
Statistics 230b, Introductory Data Analysis (SPRING)
Cross-listing: Statistics 530a, PLSC 530b
Instructor: Mr. J. Hartigan
Time: Mon, Wed 2:30 - 3:45, 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 233a, Census Data
Cross-listing: Statistics 533a
Instructor: Mr. D. Pollard
Time: Tues, Thurs 2:30 - 3:45
Place: ML 104
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.
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Statistics 241a, Probability Theory (FALL)
Cross-listing: Statistics/Mathematics 541a
Instructor: Mr. M. Wegkamp.
Time: Mon, Wed, Fri 9:30 - 10:20
Place: LC 101
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 WLH 116
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, Stochastic Processes (SPRING)
Cross-listing: Statistics 551b
Instructor: Mr. J. Chang.
Time: Mon, Wed 1 - 2:15 LOM 206
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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Statistics 312a, Linear Models (FALL)
Cross-listing: Statistics 612a
Instructor: Mr. P. Barbe.
Time: Tues, Thurs Th 9:-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.
Statistics 361b, Data Analysis (SPRING)
Cross-listing: Statistics 661b
Instructor: Mr. P. Barbe.
Time: Mon, Wed 2:30 - 3:45 DL 102
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.
Statistics 364b, Information Theory (SPRING)
Cross-listing: Statistics 664b
Instructor: Mr. A. Barron.
Time: Tues, Thurs 9-10:15 24 Hillhouse
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, redunda ncy, mutual information, channel
capacity. Basic theorems of data compression, data summarization, and
channel coding. Applications in statistics and finance. After statistics
Statistics 241.
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INFORMATION]
Statistics 600b, Advanced Probability (SPRING)
Cross-listing: Statistics 330b
Instructor: Mr. D. Pollard.
Time: Tues, Thurs 2:30 - 3:45 24 Hillhouse
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 603b, Stochastic Calculus (SPRING)
Instructor: Mr. J. Chang.
Time: T TH 4 - 5:15 24 Hillhouse
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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INFORMATION]
Statistics 605b, Foundations of Statistics (SPRING)
Instructor: Mr. J. Hartigan
Time: T TH 1 - 2:15 24 Hillhouse
This course will investigate philosophical and historical issues in the
foundations of statistics. The origins and evolution of probability. The
bayesian-frequentist dichotomy. Is decision theory necessary or useful? Is
robustness possible? Are asymptotic results applicable? How are independence
assumptions justified, and what to do if they are not? Puzzles and paradoxes.
The likelihood and invariance principles. Fiducial inference. Practical
probability.
Statistics 608a, Approximation of probability distributions (FALL)
Instructor: Mr. P. Barbe.
Time: Tues, Thurs 12 - 1
Place: 24 HH Rm. 107
A careful study of methods for establishing probability inequalities
and approximations. Topics include concentration inequalities, coupling, Stein's method,
Edgeworth expansions, Laplace's method, saddle point approximations.
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Statistics 610a, Statistical Inference (FALL)
Instructor: Mr. M. Wegkamp.
Time: Mon, Wed 1 - 2:15
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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Statistics 625a, Statistical Case Studies (FALL)
Instructor: Mr. D. Pollard.
Time: Wed 2:30 - 4:30
Place: 24 HH Rm. 107
The course will be run as a complement to introductory Census Data course (Stat 233/533). A detailed study
of the sources and uses of Census data. Knowledge of Statistics at the Stat 242 level assumed. Students will need to
work with Splus, Perl and other sofware for data extraction and mapping.
[MORE COURSE INFORMATION]
Statistics 626b, Practical Work (SPRING)
Instructor: Mr. N. Hengartner. FRIDAY 11am 24 Hillhouse
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 690a, Introduction to Research (FALL)
Instructor: Mr. A. Barron.
Time: Tues, Thurs 12 - 1
Place: 24 HH Rm. 107
Formulation and development of research topics. Students will read and review
literature and give oral presentations. Discussion of methods to address open
problems in statistics.
Statistics 700, Departmental Seminar
Time: Monday 4:15-
Important activity for all members of the department.
24 Hillhouse Avenue. See
weekly seminar announcements.