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Course Announcements |
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| Last revised: 8/28/08 | |
AUTUMN 2008
STATISTICS 20000. Elementary Statistics. This course meets one of the general education requirements in the mathematical sciences. STAT 20000 may not be used in the statistics major. It is recommended for students who do not plan to take advanced statistics courses. Not open to students with credit for STAT 22000 or 23400. 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. STATISTICS 22000=HDCP 22050. Stat Meth And Applications. Students who matriculate in the College after September 2008 may count either STAT 22000 or STAT 23400, but not both, toward the forty-two credits required for graduation. 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. STATISTICS 22400=HSTD 32400. Applied Regression Analysis This course is an introduction to the methods and applications of fitting and interpreting multiple regression models. The main emphasis is on the method of least squares. Topics include the examination of residuals, the transformation of data, strategies and criteria for the selection of a regression equation, the use of dummy variables, and tests of fit. The techniques discussed will be illustrated by many real examples involving biological and social science data. Examples and exercises will be implemented in a statistical software package "Stata", but familiarity with Stata is not required. STATISTICS 23400. Statistical Models and
Methods. Students who matriculate in the College after September 2008 may count either STAT 22000 or STAT 23400, but not both, toward the forty-two credits required for graduation. 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). Students with AP Calculus credit for any of these prerequisite courses may also enroll. 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 19520-19620 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. STATISTICS 24400. Statistical Theory/Method-1 This is the first quarter of a two-quarter sequence. Enrollment in the first quarter alone is permitted, although not recommended. The first quarter will cover the essential tools from probability needed for study of statistical theory and the basic elements of statistical theory. Topics will include the definitions of probability and random variables, binomial and other discrete probability distributions, normal and other continuous probability distribution, joint probability distributions and the transformation of random variables, principles of inference (including Bayesian inference), maximum likelihood estimation, hypothesis testing and confidence intervals, likelihood ratio tests, multinomial distributions and chi-square tests. Some large sample theory will be included. The emphasis will be upon statistical theory, specifically upon concepts and tools that are useful for understanding and applying statistical methodology. There is no enforced prerequisite in probability or statistics, although the pace is such that students may find it useful to have taken a previous elementary course. The coverage of topics in probability will be limited, so that those who have taken a course in probability will find reinforcement rather than redundancy. The second quarter will cover statistical methodology, including some multivariate analysis, the analysis of variance, the regression phenomenon, linear regression analysis, data analysis, and correlation. The mathematics prerequisites are listed as general guidance. You should be comfortable with multivariate calculus through partial differentiation and multiple integration. Linear algebra is generally used only in 24500 and not 24400. Graduate CoursesSTATISTICS 30400. Distribution Theory This course covers the basics of distribution theory. Topics include:
STATISTICS 30700=CMSC 37800. Numerical Computation
This course starts with a presentation of the fundamental algorithms for the solution of linear equations, the decomposition of matrices, and finite dimensional eigenvalue problems. Applications to least squares/regression will be presented, emphasizing use of existing numerical software. The course will also discuss optimization problems and introduce the basic principles of simulation-based methods. Topics include:
By the end of the course students should be able to apply these algorithms in their research work. STATISTICS 32300=HSTD 43000. Bayesian Methods and Computation. This course will cover basics of modern statistical computation, with emphasis on Bayesian computational methods. It will begin with the introduction to Bayesian statistics, and cover normal and non-normal approximation to likelihood and posterior distributions, the EM algorithm, data augmentation and Markov Chain Monte Carlo (MCMC) methods. Time permitting, we will conclude with some recent developments in the MCMC area, such as perfect and adaptive sampling methods. Biostatistical and environmental examples will be given throughout the course. There will be weekly homeworks, and students will be expected to complete a project by the end of the course. There will be no final exam, but there will be an in-class final project presentation. Algorithms can be implemented in any language, but familiarity with R and Matlab will be assumed. STATISTICS 33500=GSBC 41910. Time-Series Analysis/Forecast
GSB Honor Code: This course requires students to follow the GSB Honor Code and Standards of Scholarship in examinations and homework assignments. The GSB Honor Code requires students to sign the following pledge, "I pledge my honor that I have not violated the Honor Code during this examination," on every examination. Course Ob jective:
Lecture: Thursdays 8:30 to 11:30 am, starting September
25 STATISTICS 33610. Asymptotics for Time
Series. STATISTICS 34300. Applied Linear Stat Methods Statistics 34300 is an intensive course in the theory and methods of linear regression and related techniques of statistical modelling. It is intended primarily for graduate students in Statistics and related fields. The course is also open to undergraduates and others who have a solid understanding of matrix algebra and basic statistical theory. Thorough familiarity with the simple linear regression model is expected. The course will review linear regression with a single predictor, and will cover the multiple-predictor case; least-squares estimation; associated distribution theory; estimation, confidence intervals and tests; regression with errors in the predictors; weighted least squares, assessing lack of fit; residual analysis; regression diagnostics; transformations; model building; collinearity; subset-selection methods, including stepwise regression; prediction; nonlinear least squares. STATISTICS 35000=HSTD 30900, ENST 27400, PPHA 36400. Principles
of Epidemiology. Epidemiology is the study of the distribution and determinants of health and disease in human populations. This course introduces the basic principles of epidemiologic study design, analysis and interpretation through lectures, assignments, and critical appraisal of both classic and contemporary research articles. OBJECTIVES
STATISTICS 35201=HSTD 32901. Intro
to Clinical Trials. This course will review major components of clinical trial conduct, including the formulation of clinical hypotheses and study endpoints, trial design, development of the research protocol, trial progress monitoring, analysis, and the summary and reporting of results. Other aspects of clinical trials to be discussed include ethical and regulatory issues in human sub jects research, data quality control, meta-analytic overviews and consensus in treatment strategy resulting from clinical trials, and the broader impact of clinical trials on public health. The course will be conducted partly via lectures and partly in a ‘reading’ format. Designated individuals may take the lead in covering a main topic, with participation and input by all. Similarly, presentation of materials in the special topics portion of the class meeting will be shared among all. STATISTICS 36900=HSTD 33300. Longitudinal Data Analysis. Longitudinal data consist of multiple measures over time on a sample of individuals. This type of data occurs extensively in both observational and experimental biomedical and public health studies, as well as in studies in sociology and applied economics. This course will provide an introduction to the principles and methods for the analysis of longitudinal data. Emphasis will be on data analysis and interpretation. Supporting statistical theory will be given at a level appropriate for an advanced Master’s student in Statistics. Problems will be motivated by applications in epidemiology, clinical medicine, health services research, and disease natural history studies. STATISTICS 38100. Measure-Theoretic Probability I. This course is the first of a three quarter sequence presenting a careful development of some topics from measure and probability. Topics to be covered in 381 include: classes of sets -- fields, sigmafields, monotone classes, pi and lambda systems; probabilities and general measures; independence and the Borel-Cantelli lemmas; measurable functions; induced measures, distribution and inverse distribution functions; integration with respect to measures -- basic properties, change of variable, indefinite integration, densities; integration to the limit -- MCT, DCT, and friends; laws of large numbers, applications to probability and statistics; transition probabilities and product measures. STATISTICS 39000=FINM 34500. Stochastic Calculus
I. This course is an introduction to stochastic calculus as it is relevant to the pricing and hedging of options and other derivative securities. It is the first of a two-quarter sequence offered in collaboration by the Department of Statistics and the master's program in Mathematical Finance. The main topics to be covered are:
There will be weekly homework assignments, and midterm and final exams. The course assistants will conduct weekly help sessions on Friday afternoons. STATISTICS 47620. Simulation Methods. This will be a brief introduction to several useful techniques of simulation:
The utility of these methods will be illustrated by a number of substantial examples, including
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