SPRING 2008

College Courses

STATISTICS 20000.  Elementary Statistics.
Sec 01: Dan Wang, MWF 9:30-10:20 AM, Eckhart 133.
Sec 02: Wenlong Wang, MWF 12:30-1:20 PM, Eckhart 133.
PQ:  Math 10500 or equivalent.
Required reading: Statistics, 4th edition, by Freedman, Pisani, Purves 2007, Norton.
ISBN-10: 0393929728, ISBN-13: 978-0393929720.

This course meets one of the general education requirements in the mathematical sciences. NOTE: STAT 20000 may not be used in the statistics major. It is recommended for students who do not plan to take advanced statistics courses. This course introduces statistical concepts and methods for the collection, presentation, analysis, and interpretation of data. Elements of sampling, simple techniques for analysis of means, proportions, and linear association are used to illustrate both effective and fallacious uses of statistics.

Back to top

STATISTICS 22000. Statistical Methods and Their Applications.
Sec 01:  Zuoheng Wang, MWF 10:30-11:20 AM, Harper Memorial 140.
Sec 02:  Shali Wu, MWF 1:30-2:20 PM, Eckhart 133.
PQ:  2 QTRS Calculus.
Required reading: Introduction to the Practice of Statistics, 5th edition by Moore and McCabe
2006, W. H. Freeman. ISBN-10: 0716764008, ISBN-13: 978-0716764007

This course introduces statistical techniques and methods of data analysis, including the use of computers. Examples are drawn from the biological, physical, and social sciences. Students are required to apply the techniques discussed to data drawn from actual research. Topics include data description, graphical techniques, exploratory data analyses, random variation and sampling, one- and two-sample problems, the analysis of variance, linear regression, and analysis of discrete data.

Back to top

STATISTICS 22200 Linear Models and Experimental
Sec 01: Linda Collins, TTh, 9:00-10:20 AM, Eckhart 133.
PQ: STAT 22000 or consent of instructor.
Required Reading: Oehlert, G. W. (2000) A First Course in Design and Analysis of Experiments. W. H. Freeman. ISBN-10: 0-7167-3510-5 ISBN-13: 978-0-7167-3510-6.

This course covers principles and techniques for the analysis of experimental data and the planning of the statistical aspects of experiments. Topics include linear models, analysis of variance, randomization, blocking, factorial designs, confounding, and incorporation of covariate information.

In addition to regular homework assignments and exams, students will be required to complete a project involving the design and analysis of an experiment of their own.

Back to top

STATISTICS 23400. Statistical Models and Methods.
Sec 01: Pending, MWF, 11:30-12:20 PM, Eckhart 133.
Sec 02: Michael Finegold, MWF, 2:30-3:20 PM, Eckhart 133.
PQ: Mathematics 13300, 15300 or 16300.
Required Reading: Investigating Statistical Concepts, Applications, and Methods by Chance and Rossman 2006, Duxbury (Thomson Brooks/Cole). ISBN-10: 0495050644, ISBN-13: 978-D495050643.

A Brief Course in Mathematical Statistics by Tanis and Hogg 2007, Prentice Hall, ISBN-10: 0131751395, ISBN-13: 978-0131751392.

This course presents basic ideas of probability theory and statistics, and is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations, and an introduction to conditional expectation. Binomial, hypergeometric, Poisson, exponential, normal and other standard probability distributions are considered. Some probability models are studied mathematically and others via simulation on a computer. Sampling distributions and related statistical methods are explored mathematically, studied via simulation and illustrated on data. Statistical methods for describing data and making inferences based on samples from populations are presented. Methods include, but are not limited to, inference for proportions and means for one- and two-sample problems, correlation and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics is employed to the level of univariate calculus and is less demanding than that required by STAT 24400.

Univariate calculus and computer simulation are used throughout the course to investigate statistical concepts and their mathematical underpinnings. One full year of univariate calculus is a prerequisite for the course (Math 13300, 15300, or 16300). Familiarity with at least limits, derivatives and integrals of polynomial and exponential functions, change of variable (substitution) in definite integrals, max-min problems, use of summation notation, and sequences and series as well as a willingness to explore ideas mathematically are key to your success in this course. See http://statistics.uchicago.edu/~stat234 for more detailed information.

Stat 23400 takes the mathematical prerequisite quite seriously. Enrolling concurrently in either Math 13300, 15300, or 16300 while taking Stat 23400 is very strongly discouraged. Further, students who do not feel strong mathematically, may want to wait until completing their entire mathematical requirement (e.g., Math 19500-19600 for Economics majors) before enrolling in Stat 23400. Economics majors are strongly encouraged to delay taking Stat 23400 until the quarter just before enrolling in their required econometrics course (Econ 21000), for which Stat 23400 is a prerequisite. Thus, delaying Stat 23400 until at least late in the second year or even early in the third year of the Economics degree program should not be considered unusual.

Back to top

This is the second quarter of a two-quarter sequence. Enrollment in the second quarter alone is permitted, although not recommended. The first quarter covered the basics -- tools from probability and the elements of statistical theory. The second quarter will cover statistical methodology, including the data transformation, regression phenomena, t-tests, analysis of variance, linear regression, correlation, and some multivariate distribution theory. Some principles of data analysis will be introduced, and an attempt will be made to present ANOVA and regression in a unified framework. Much of the material is covered in chapters 10-12 and 14 of the text, but other viewpoints and derivations will be introduced as well. The computer will be used in the second quarter. Some mathematical maturity will be assumed, to the level of calculus. The computer will be used for data analysis and simulation.

Back to top

STATISTICS 24600. Statistical Theory and Methods 3.
Sec 01: Yali Amit,  TTh, 10:30-11:50 AM, Eckhart 133.
PQ: STAT 24400 and STAT 24500 or consent of instructor.
Required Reading: Pattern Recognition and Machine Learning, Bishop, Christopher M. 2006, Springer Verlag. ISBN-10: 0387310738, ISBN-13: 978-0387310732.

This course will introduce a variety of modern statistical methods. We will start with the description of
families of multivariate models - multivariate normal distribution, graphical models, log-linear models.
We will then discuss inference for such models including the EM algorithm, Bayesian inference and Monte-carlo methods. The main application of the models will be in classification problems - i.e the 'generative approach'. We will also introduce some modern discriminative approaches for classification.

Back to top

STATISTICS 25100.  Intro to Math Probability.
Sec 01: Michael J. Wichura, TTh, 12:00-1:20 PM, Eckhart 312.
PQ: MATH 20000 or MATH 20400 or consent of instructor.
Required Reading: A First Course in Probability, 7th edition, Ross, S. 2005, Pearson/Prentice Hall.
ISBN-10: 0131856626, ISBN-13: 978-0131856622.

The aim of the course is to provide an introduction to the concepts of probability. The course will cover the basic ideas used to describe aspects of randomness, such as events, random variables, independence, and conditional probability, with emphasis on the methods, calculation, and applications of probability. The topics treated are: combinatorics; probability models; rules of probability; conditional probability; independence; random variables; expectation and standard deviation; games of chance; common discrete distributions --- uniform, binomial, hypergeometric, geometric, negative binomial, and Poisson --- and their interrelationships; univariate and multivariate density and distribution functions, change of variable formulas for densities, common continuous distributions --- beta, Cauchy, chisquare, exponential, gamma, lognormal, normal, uniform --- and their interrelationships; moment generating functions, laws of large numbers and the central limit theorem.

For more information, please visit http://galton.uchicago.edu/~wichura/Stat251/courseinfo.html

Back to top

STATISTICS 26100=STATISTICS 33600. Time Dependent Data.
Sec 01: MIchael Stein, TTh, 1:30-2:50 PM, Eckhart 202.
PQ: STAT 24400 or STAT 24500.
Required Reading: Time Series Analysis and Its Applications: With R Examples, 2nd edition
Shumway and Stoffer 2006, Springer. ISBN-10: 0387293175, ISBN-13: 978-0387293172.

This course considers the modeling and analysis of data that are ordered in time. The main focus will be on quantitative observations taken at evenly spaced intervals and will include both time-domain and spectral approaches. Time permitting, statistical approaches to other data types, such as categorical observations or point processes, will be considered.

Back to top

STATISTICS 26700=CHSS 32900, HIPS 25600, STAT 36700.
Sec 01: Stephen Stigler, MWF, 9:30-10:20 AM, Eckhart 117.
PQ: A course in Statistics.
Required Reading: The History of Statistics: The Measurement of Uncertainty Before 1900.
Stigler, Stephen M. 1990, Belknap Press of Harvard University Press. ISBN-10: 067440341X,
ISBN-13: 978-0674403413. Other materials will be distributed in class or by web.

This course will cover topics in the history of statistics, from the eleventh century to the middle of the twentieth century. The emphasis will be upon the period 1650 to 1950, and upon the mathematical developments in the theory of probability and how they came to be used in the sciences, both to quantify uncertainty in observational data and as a conceptual framework for scientific theories. The course will include broad views of the development of the subject, and closer looks at specific people and investigations, including reanalyses of historical data. Topics will include: Early probability; Probability in seventeenth century medicine; Inverse probability in inference; Statistical methods in early geodesy; The introduction of least squares; Statistics in social science; Statistics in early biology and psychology; Simulation; Statistics and the evaluation of social programs; Maximum likelihood estimation; Statistics and nineteenth century forensic science; Statistics and medical science. Major figures who will be examined include Pascal, various Bernoullis, De Moivre, Laplace, Boscovich, Gauss, Quetelet, Galton, Edgeworth, Karl Pearson, Gosset, Fisher, Neyman.

Back to top

Graduate Courses

STATISTICS 30200. Mathematical Statistics 2.
Sec 01:  Wei Biao Wu, MW, 1:30-2:50 PM,  Eckhart 117.
PQ:  STAT 30100 or consent of instructor.
Required Reading: Mathematical Statistics, 2nd ed. 2003. Corr. 4th printing edition (October 5, 2007). Jun Shao, Springer. ISBN-10:0387953825, ISBN-13: 978-0387953823.

This course continues the development of mathematical statistics. Topics of importance include: statistical decision theory, admissability and the Neyman-Pearson lemma, formal hypothesis testing, comparison with conditional frequentist and Bayesian viewpoints, ancillarity, interval estimation, UMP tests and MLR, unbiased tests, score statistics, generalized likelihood ratio tests and asymptotics, minimax estimation, empirical Bayes and "shrinkage".

Back to top

STATISTICS 31300.  Introduction to Stochastic Processes 2.
Sec 01: Per A. Mykland, TTh, 10:30-11:50 AM,  Eckhart 117.
PQ:  STAT 31200 or consent of instructor.
Required reading:

Back to top

STATISTICS 32200. Bayesian Data Analysis.
Sec 01: Matthew Stephens, MW, 1:30-2:50 PM, Eckhart 308.
PQ:  Consent of instructor.
Reading: There is no required text, but "Bayesian Theory" by Bernardo and Smith is background reading.

This course is aimed at graduate students in statistics, and others with the necessary statistical background. We will assume familiarity with standard statistical distributions (e.g. Normal, Poisson, Binomial, Exponential), with the laws of probability, and concepts of statistical inference (maximum likelihood estimation, hypothesis testing, confidence intervals, etc), and basic familiarity with the R statistical package.

The course will cover foundations of Bayesian statistics, including axiomatic development, exchangeability, De Finetti's theorem, Jeffreys and improper priors, decision theory, Bayesian hypothesis testing and Bayes factors. Concepts will be illustrated mainly by instructive "toy" examples, where calculations can be done by hand. However, we will also study more complex, practical applications of Bayesian statistics. Although methods of computation will be discussed, the primary focus will be on concepts, and not on computation.

Back to top

STATISTICS 33200=HSTD 43200. Causal Inference.
Sec 01: Tyler Vanderweele, TTh, 10:30-11:50 AM, BSLC arr.
PQ: STAT 22400-22600 or HSTD 32400-32700 or equivalent or consent of instructor.
Required reading: 

Back to top

STATISTICS 34700. Generalized Linear Models.
Sec 01:  Peter McCullagh, TTh, 3:00-4:20 PM, Eckhart 133.
PQ: STAT 34300 or consent of instructor.
Required Reading: Generalized Linear Models, 2nd edition, McCullagh and Nelder 1990, Chapman & Hall/CRC. ISBN-10: 0412317605, ISBN-13: 978-0412317606.
Recommended Reading: Modern Applied Statistics with S, 4th edition, Venables and Ripley 2003, Springer. ISBN-10: 0387954570, ISBN-13: 978-0387954578.
Applied Statistics: Principles and Examples, Cox and Snell 19821 Chapman & Hall/CRC. ISBN-10: 0412165708, ISBN-13: 978-0412165702.

This is an applied course for students who are familiar with linear models at the level of Draper and Smith or Weisberg. The following topics will be covered:

Factors, variates, contrasts, interactions
Exponential-family models: variance function
Definition of a generalized linear model: link functions
Analysis of deviance
Specific examples of GLMs

logistic and probit regression
cumulative logistic models
log-linear models and contingency tables
inverse linear models

Quasi-likelihood and least squares; estimating functions
Over-dispersion
Partially linear models

Back to top

STATISTICS 35500. Statistical Genetics.
Sec 01: Mary Sara McPeek, F, 1:30-4:10 PM, Eckhart 117.
PQ: Human Genetics 471 and Statistics 244 and 245. Students who do not meet the prerequisites may enroll on a P/NP basis with consent of the instructor.
Reading: There is no textbook.

This is an advanced course in statistical genetics. Prerequisites are Human Genetics 471 and Statistics 244 and 245. Students who do not meet the prerequisites may enroll on a P/NP basis with consent of the instructor. This is a discussion course and student presentations will be required. Topics vary and may include, but are not limited to, statistical problems in association mapping, linkage mapping, population genetics, microarray analysis, and genetic models for complex traits.

Back to top

STATISTICS 36700=CHSS 32900, HIPS 25600, STAT 26700.
Sec 01: Stephen Stigler, MWF, 9:30-10:20 AM, Eckhart 117.
PQ: A course in Statistics.
Required Reading: Stephen M. Stigler, The History of Statistics: The Measurement of Uncertainty Before 1900. (Cambridge, Mass.: Harvard University Press, 1986.) (Available in paperback.) Other materials will be distributed in class or by web.

This course will cover topics in the history of statistics, from the eleventh century to the middle of the twentieth century. The emphasis will be upon the period 1650 to 1950, and upon the mathematical developments in the theory of probability and how they came to be used in the sciences, both to quantify uncertainty in observational data and as a conceptual framework for scientific theories. The course will include broad views of the development of the subject, and closer looks at specific people and investigations, including reanalyses of historical data. Topics will include: Early probability; Probability in seventeenth century medicine; Inverse probability in inference; Statistical methods in early geodesy; The introduction of least squares; Statistics in social science; Statistics in early biology and psychology; Simulation; Statistics and the evaluation of social programs; Maximum likelihood estimation; Statistics and nineteenth century forensic science; Statistics and medical science. Major figures who will be examined include Pascal, various Bernoullis, De Moivre, Laplace, Boscovich, Gauss, Quetelet, Galton, Edgeworth, Karl Pearson, Gosset, Fisher, Neyman.

Back to top

STATISTICS 38600. Topics in Stochastic Processes.
Sec 01: Steven P. Lalley, TTh, 3:00-4:20 PM, Eckhart 117.
PQ: Consent of instructor.
Recommended Reading: Stochastic Differential Equations by Oksendal (PG)
Brownian Motion and Stochastic Calculus by Karatzas and Shreve (R)
Foundations of Modern Probability by Kallenberg (X)

Topics:

Wiener process (Brownian motion)
Weak Convergence in Function Space
Ito Integral and Ito Calculus
Introduction to Diffusion Processes

Back to top

STATISTICS 43800. Statistical Inference for Financial Data.
Sec 01: Per A. Mykland, TTh, 1:30-2:50 PM, Eckhart 117.
PQ: STAT 30200, 34300 and 38300 or consent of instructor.
Required Reading:

Back to top