FACULTY OF THE DEPARTMENT OF STATISTICS
Professors †Donald Andrews, Andrew Barron, Joseph Chang, John Hartigan (Emeritus), †Theodore Holford, †Peter Phillips, David Pollard, †Heping Zhang, †Hongyu Zhao, Huibin Zhou
Associate Professors John Emerson (Adjunct), †Sekhar Tatikonda
Assistant Professors Jessi Cisewski, Sahand Negahban
Senior Lecturer Jonathan Reuning-Scherer
†A joint appointment with primary affiliation in another department or school.
Statistics is the science and art of prediction and explanation. The mathematical foundation of statistics lies in the theory of probability, which is applied to problems of making inferences and decisions under uncertainty. Practical statistical analysis also uses a variety of computational techniques, methods of visualizing and exploring data, methods of seeking and establishing structure and trends in data, and a mode of questioning and reasoning that quantifies uncertainty.
The Statistics program at Yale is a blend of the mathematical theory of probability and statistical inference, the philosophy of inference under uncertainty, computational techniques, the practice of data analysis, and statistical analysis applied to economics, biology, medicine, engineering, and other areas. Statistical methods are widely used in the sciences, medicine, industry, business, and government; graduates can work in these areas or go on to graduate study.
The curriculum for the Statistics major is a synthesis of theory, methods, and applications. The requirements are designed to achieve balance and depth in each of the three directions of probability, statistics, and data analysis. Statistics can be taken either as a primary major or as one of two majors, in consultation with the director of undergraduate studies. Appropriate majors to combine with Statistics include programs in the social sciences, natural sciences, engineering, computer science, or mathematics. A Statistics concentration is also available within the Applied Mathematics major.
Prerequisites Multivariable calculus and linear algebra are required and should be taken before or during the sophomore year. This requirement may be satisfied by MATH 120 and MATH 222 or 225, or equivalents.
Requirements of the major for the B.A. degree The B.A. degree program requires ten term courses beyond the prerequisites, including the senior project. Majors take two courses in the theory and applications of probability (STAT 241 and 251), two courses emphasizing the theory of statistical inference (STAT 242 and 312), and two courses in the methods and practice of data analysis, chosen from STAT 230, 361, and 363. STAT 238 may be substituted for STAT 241 with the permission of the director of undergraduate studies. All majors are also required to take a course in computing (ENAS 130 or CPSC 112). The two remaining courses are electives chosen from Statistics courses numbered above 200. Appropriate courses in other departments or in the Graduate School may count toward the major with permission of the director of undergraduate studies.
Requirements of the major for the B.S. degree The B.S. degree program requires twelve term courses beyond the prerequisites. In addition to the courses indicated for the B.A. degree, the B.S. degree requires a course in mathematical analysis (MATH 260, 300, or 301) and an additional Statistics elective numbered above 200.
Senior requirement In the senior year, majors in both degree programs complete a research project in STAT 490. Students enrolled in this course work on a research project under the supervision of a faculty member, present and share their progress with each other during the seminar meetings, and write a final report.
Credit/D/Fail A maximum of one course taken Credit/D/Fail may be counted toward the requirements of the major, with permission of the director of undergraduate studies.
REQUIREMENTS OF THE MAJOR
Number of courses B.A.—10 term courses beyond prereqs (incl senior project); B.S.—12 term courses beyond prereqs (incl senior project)
Distribution of courses B.A.— 2 Stat electives numbered above 200, as specified; B.S.—3 Stat electives numbered above 200, as specified
Senior requirement Both degrees—Senior project (STAT 490)
STAT 101—106, Introduction to Statistics
A basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course in this group 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 first seven weeks of classes are attended by all students in STAT 101–106 together, as general concepts and methods of statistics are developed. The remaining weeks are divided into field-specific sections that 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. No prerequisites beyond high school algebra. May not be taken after STAT 100 or 109.
Students enrolled in STAT 101–106 who wish to change to STAT 109, or those enrolled in STAT 109 who wish to change to STAT 101–106, must submit a course change notice, signed by the instructor, to their residential college dean by Friday, October 2. The approval of the Committee on Honors and Academic Standing is not required.
STAT 101a / E&EB 210a, Introduction to Statistics: Life Sciences Walter Jetz
Statistical and probabilistic analysis of biological problems, presented with a unified foundation in basic statistical theory. Problems are drawn from genetics, ecology, epidemiology, and bioinformatics.
STAT 102a / EP&E 203a / PLSC 452a, Introduction to Statistics: Political Science Jonathan Reuning-Scherer
Statistical analysis of politics, elections, and political psychology. Problems presented with reference to a wide array of examples: public opinion, campaign finance, racially motivated crime, and public policy.
STAT 103a / EP&E 209a / PLSC 453a, Introduction to Statistics: Social Sciences Jonathan Reuning-Scherer
Descriptive and inferential statistics applied to analysis of data from the social sciences. Introduction of concepts and skills for understanding and conducting quantitative research.
STAT 105a, Introduction to Statistics: Medicine Jonathan Reuning-Scherer
Statistical methods used 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 106, Introduction to Statistics: Data Analysis ]
Courses in Statistics
STAT 100b, Introductory Statistics Jessica Cisewski
An introduction to statistical reasoning. Topics include numerical and graphical summaries of data, data acquisition and experimental design, probability, hypothesis testing, confidence intervals, correlation and regression. Application of statistical concepts to data; analysis of real-world problems. May not be taken after STAT 101–106 or 109.
EPE: Intro Statistics
STAT 109a, Introduction to Statistics: Fundamentals Jonathan Reuning-Scherer
General concepts and methods in statistics. Meets for the first half of the term only. May not be taken after STAT 100 or 101–106.
½ Course cr
STAT 230a or b, Introductory Data Analysis Staff
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used. Prerequisite: a 100-level Statistics course or equivalent, or with permission of instructor.
EPE: Intro Statistics
STAT 238a, Probability and Statistics Joseph Chang and Xiaofei Wang
Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis. Essentials of probability, including conditional probability, random variables, distributions, law of large numbers, central limit theorem, and 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 for calculations, simulations, and analysis of data. After MATH 118 or 120.
STAT 241a / MATH 241a, Probability Theory Yihong Wu
Introduction to probability theory. Topics include probability spaces, random variables, expectations and probabilities, conditional probability, independence, discrete and continuous distributions, central limit theorem, Markov chains, and probabilistic modeling. After or concurrently with MATH 120 or equivalent.
STAT 242b / MATH 242b, Theory of Statistics Andrew Barron
Study of the principles of statistical analysis. Topics include maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. Some statistical computing. After STAT 241 and concurrently with or after MATH 222 or 225, or equivalents.
STAT 251b / ENAS 496b / MATH 251b, Stochastic Processes Sahand Negahban
Introduction to the study of random processes including linear prediction and Kalman filtering, Poison counting process and renewal processes, Markov chains, branching processes, birth-death processes, Markov random fields, martingales, and random walks. Applications chosen from communications, networking, image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, and genetics and evolution. Prerequisite: STAT 241 or equivalent.
STAT 262a / AMTH 262a / CPSC 262a, Computational Tools for Data Science Daniel Spielman and Sahand Negahban
Introduction to the core ideas and principles that arise in modern data analysis, bridging statistics and computer science and providing students the tools to grow and adapt as methods and techniques change. Topics include principle component analysis, independent component analysis, dictionary learning, neural networks, clustering, streaming algorithms (streaming linear algebra techniques), online learning, large scale optimization, simple database manipulation, and implementations of systems on distributed computing infrastructures. Students require background in linear algebra, multivariable calculus, and programming. after or concurrently with MATH 222, 225, or 231; after or concurrently with MATH 120, 230, or ENAS 151; after or concurrently with CPSC 100, 112, or ENAS 130.
STAT 312a, Linear Models David Pollard
The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms, with particular reference to the R statistical language. After STAT 242 and MATH 222 or 225.
* STAT 325a, Statistical Case Studies Xiaofei Wang
Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R. Prerequisite: STAT 230, 361, or equivalent.
STAT 330b / MATH 330b, Advanced Probability David Pollard
Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis assumed.
STAT 361b / AMTH 361b, Data Analysis Jessica Cisewski
Selected topics in statistics explored through analysis of data sets using the R statistical computing language. Topics include linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering. After STAT 242 and MATH 222 or 225, or equivalents.
STAT 363b, Multivariate Statistics for Social Sciences Jonathan Reuning-Scherer
Introduction to the analysis of multivariate data as applied to examples from the social sciences. Topics include principal components analysis, factor analysis, cluster analysis (hierarchical clustering, k-means), discriminant analysis, multidimensional scaling, and structural equations modeling. Extensive computer work using either SAS or SPSS programming software. Prerequisites: knowledge of basic inferential procedures and experience with linear models.
STAT 364b / AMTH 364b / EENG 454b, Information Theory Yihong Wu
Foundations of information theory in 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 STAT 241.
STAT 365b, Data Mining and Machine Learning Xiaofei Wang
Techniques for data mining and machine learning from both statistical and computational perspectives, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. Discussion includes 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 to apply them to data. After STAT 242.
* STAT 480a or b, Individual Studies Staff
Directed individual study for qualified students who wish to investigate an area of statistics not covered in regular courses. A student must be sponsored by a faculty member who sets the requirements and meets regularly with the student. Enrollment requires a written plan of study approved by the faculty adviser and the director of undergraduate studies.
* STAT 490b, Senior Seminar and Project Andrew Barron
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.
Graduate Courses of Particular Interest to Undergraduates
Courses in the Graduate School are open to qualified undergraduates. Descriptions of graduate courses in Statistics are available on the departmental Web site. Permission of the instructor and of the director of graduate studies is required.