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| Statistics | |
| STAT 1100 | Chance: An Introduction to Statistics (3.00) |
| Studies introductory statistics and probability, visual methods for summarizing quantitative information, basic experimental design and sampling methods, ethics and experimentation, causation, and interpretation of statistical analyzes. Applications use data drawn from current scientific and medical journals, newspaper articles, and the Internet. Students will not receive credit for both STAT 1100 and STAT 1120. Course was offered Fall 2012, Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010, Fall 2009 | |
| STAT 1120 | Introduction to Statistics (3.00) |
| Includes graphical displays of data, relationships in data, design of experiments, causation, random sampling, probability distributions, inference, confidence intervals, tests of hypotheses, and regression and correlation. Students will not receive credit for both STAT 1100 and STAT 1120. | |
| STAT 2020 | Introduction to Biostatistics (3.00) |
| This course includes a basic treatment of probability, and covers inference for one and two populations, including both hypothesis testing and confidence intervals. Analysis of variance and linear regression are also covered. Applications are drawn from biology and medicine. Course was offered Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010 | |
| STAT 2120 | Introduction to Statistical Analysis (4.00) |
| Offered Fall 2013 | Introduction to the probability and statistical theory underlying the estimation of parameters and testing of statistical hypotheses, including those arising in the context of simple and multiple regression models. Students will use computers and statistical programs to analyze data. Examples and applications are drawn from economics, business, and other fields. Students will not receive credit for both STAT 2120 and ECON 3710. Prerequisite: MATH 1210 or equivalent; co-requisite: Concurrent enrollment in a discussion section of STAT 2120. Course was offered Spring 2013, Fall 2012, Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010, Fall 2009 |
| STAT 2559 | New Course in Statistics (1.00 - 4.00) |
| This course provides the opportunity to offer a new topic in teh subject area of statistics. Course was offered Fall 2009 | |
| STAT 3010 | Statistical Computing and Graphics (3.00) |
| Introduces statistical computing using S-PLUS. Topics include descriptive statistics for continuous and categorical variables, methods for handling missing data, basics of graphical perception, graphical displays, exploratory data analysis, and the simultaneous display of multiple variables. Students should be experienced with basic text-editing and file manipulation on either a PC or a UNIX system, and with either a programming language (e.g. BASIC) or a spreadsheet program (e.g. MINITAB or EXCEL). Credit earned in this course cannot be applied toward a graduate degree in statistics. Prerequisite: STAT 1100 or 1120 or instructor permission. | |
| STAT 3080 | From Data to Knowledge (4.00) |
| Offered Fall 2013 | Most elementary statistics courses start with a technique & present various surface level examples. This course will use relatively complicated data sets and approach them from multiple angles with elementary statistical techniques. Simulation techniques such as the bootstrap will also be used. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software. Prerequisite: An introductory statistics course. |
| STAT 3120 | Introduction to Mathematical Statistics (3.00) |
| Offered Fall 2013 | This course provides a calculus-based introduction to mathematical statistics with some applications. Topics include: sampling theory, point estimation, interval estimation, testing hypotheses, linear regression, correlation, analysis of variance, and categorical data. Prerequisite: MATH 3100 or APMA 3100. Course was offered Spring 2013, Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010 |
| STAT 3130 | Design and Analysis of Sample Surveys (3.00) |
| Discusses the main designs and estimation techniques used in sample surveys; including simple random sampling, stratification, cluster sampling, double sampling, post-stratification, and ratio estimation. Non-response problems and measurement errors are also discussed. Many properties of sample surveys are developed through simulation procedures. The SUDAAN software package for analyzing sample surveys is used. Prerequisite: STAT 1100 or 1120, MATH 3120, or instructor permission. | |
| STAT 3150 | Theory of Interest (3.00) |
| Topics include growth and time value of money, equations of value and yield rates, annuities (including contingent payments), loan amortization schedules, bonds. Additional topics are options and derivatives, as time permits. Prerequisites: MATH 1220 or MATH 1320 | |
| STAT 3220 | Introduction to Regression Analysis (3.00) |
| This course provides a survey of regression analysis techniques, covering topics from simple regression, multiple regression, logistic regression, and analysis of variance. The primary focus is on model development and applications. Prerequisite: STAT 1100 or STAT 1120 or STAT 2120. | |
| STAT 3430 | Statistical Computing with SAS (4.00) |
| Offered Fall 2013 | The course covers database management, programming, elementary statistical analysis, and report generation in SAS. Topics include: managing SAS Data Sets; DATA-step programming; data summarization and reporting using PROCs PRINT, MEANS, FREQ, UNIVARIATE, CORR, and REG; elementary graphics; introductions to the Output Delivery System, the SAS Macro language, PROC IML, and PROC SQL. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Introductory statistics course Course was offered Fall 2010 |
| STAT 3559 | New Course in Statistics (1.00 - 4.00) |
| This course provides the opportunity to offer a new topic in the subject area of Statistics. | |
| STAT 3980 | Applied Statistics Laboratory (1.00) |
| Enrollment in STAT LAB (3980) is required for all students in the department's 3000-level appled statistics courses (STAT 3080, 3220, 3430, 3130). STAT 3980 may be repeated for credit provided that a student is enrolled in at least one of these 3000-level applied courses; however, no more than one unit of STAT 3980 may be taken in any semester. | |
| STAT 4995 | Statistical Consulting (1.00 - 3.00) |
| Introduces the practice of statistical consultation. A combination of formal lectures, meetings with clients of the statistical consulting service, and sessions in the statistical computing laboratory. Students will work together with a graduate student consultant. Prerequisite: instructor permission. Course was offered Fall 2011 | |
| STAT 5000 | Introduction to Applied Statistics (3.00) |
| Introduces estimation and hypothesis testing in applied statistics, especially the medical sciences. Measurement issues, measures of central tendency and dispersion, probability, discrete probability distributions (binomial and Poisson), continuous probability distributions (normal, t, chi-square, and F), and one- and two-sample inference, power and sample size calculations, introduction to non-parametric methods, one-way ANOVA and multiple comparisons. Prerequisite: Instructor permission; corequisite: STAT 5980. Course was offered Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010, Fall 2009 | |
| STAT 5020 | Mathematical Statistics (3.00) |
| A calculus based introduction to the principles of statistical inference. Topics include sampling theory, point estimation, confidence intervals, hypothesis testing. Additional topics such as nonparametric methods or Bayesian statistics. May not be used for graduate degrees in Statistics. May not be taken if credit has been received for STAT 3120. Prerequisites: MATH 3100 or 5100 or consent of instructor. | |
| STAT 5120 | Applied Linear Models (4.00) |
| Offered Fall 2013 | Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite:STAT 3120, and either MATH 3351 or APMA 3080 |
| STAT 5140 | Survival Analysis and Reliability Theory (3.00) |
| Topics include lifetime distributions, hazard functions, competing-risks, proportional hazards, censored data, accelerated-life models, Kaplan-Meier estimator, stochastic models, renewal processes, and Bayesian methods for lifetime and reliability data analysis. Prerequisite: MATH 3120 or 5100, or instructor permission; corequisite: STAT 5980. Course was offered Spring 2011 | |
| STAT 5150 | Actuarial Statistics (3.00) |
| Covers the main topics required by students preparing for the examinations in Actuarial Statistics, set by the American Society of Actuaries. Topics include life tables, life insurance and annuities, survival distributions, net premiums and premium reserves, multiple life functions and decrement models, valuation of pension plans, insurance models, and benefits and dividends. Prerequisite: MATH 3120 or 5100, or instructor permission. Course was offered Spring 2013 | |
| STAT 5160 | Experimental Design (4.00) |
| This course develops fundamental concepts and methodology in the design and analysis of experiments. Topics include analysis of variance, multiple comparison tests, randomized block designs, Latin square and related designs, factorial designs, split-plot and related designs, and analysis of covariance. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission. Course was offered Spring 2012, Fall 2009 | |
| STAT 5170 | Applied Time Series (4.00) |
| Studies the basic time series models in both the time domain (ARMA models) and the frequency domain (spectral models), emphasizing application to real data sets. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 3120 | |
| STAT 5180 | Design and Analysis of Sample Surveys (4.00) |
| Discussion of the main designs and estimation techniques used in sample surveys: simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non response and other non sampling errors. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software. Prerequisites: STAT 3120. Course was offered Spring 2013, Fall 2010 | |
| STAT 5265 | Investment Science I (4.00) |
| Offered Fall 2013 | The course will cover a broad range of topics, with the overall theme being the quantitative modeling of asset allocation and portfolio theory. It begins with deterministic cash flows (interest theory, fixed-income securities), the modeling of interest rates (term structure of interest rates), stochastic cash flows, mean-variance portfolio theory, capital asset pricing model, and the utility theory basis for financial modeling. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using Matlab or R statistical software. Prerequisite: MATH 3100. |
| STAT 5266 | Investment Science II (4.00) |
| This course is a follow-up to Investment Science I (Stat 5265). It begins with models for derivative securities, including asset dynamics, options and interest rate derivatives. The remaining portion of the course then combines all of the ideas from the two courses to formulate strategies of optimal portfolio growth and a general theory of investment evaluation. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using Matlab or R statistical software. Prerequisite: MATH 3100, STAT 5265. Course was offered Spring 2010 | |
| STAT 5310 | Clinical Trials Methodology (3.00) |
| Studies experimental designs for randomized clinical trials, sources of bias in clinical studies, informed consent, logistics, and interim monitoring procedures (group sequential and Bayesian methods). Prerequisite: A basic statistics course (MATH 3120/5100) or instructor permission. | |
| STAT 5330 | Data Mining (4.00) |
| This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster analysis and principal components analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisites: Previous or concurrent enrollment in STAT 5120 or STAT 6120. Course was offered Spring 2012, Fall 2010 | |
| STAT 5340 | Bootstrap and Other Resampling Methods (3.00) |
| This course introduces the basic ideas of resampling methods, from jackknife and the classic bootstrap due to Efron to advanced bootstrap techniques such as the estimating function bootstrap and the Markov chain marginal bootstrap. | |
| STAT 5410 | Introduction to Statistical Software (1.00) |
| Offered Fall 2013 | This course develops basic data skills in SAS and R, focusing on data-set management and the production of elementary statistics. Topics include data input, cleaning and reshaping data, producing basic statistics, and simple graphics. The student is prepared for the development of advanced data-analysis techniques in applied statistics courses. |
| STAT 5430 | Statistical Computing with SAS (3.00) |
| The course covers database management, programming, elementary statistical analysis, and report generation in SAS. Topics include: managing SAS Data Sets; DATA-step programming; data summarization and reporting using PROCs PRINT, MEANS, FREQ, UNIVARIATE, CORR, and REG; elementary graphics; introductions to the Output Delivery System, the SAS Macro language, PROC IML, and PROC SQL. Prerequisites: Introductory statistics course. Course was offered Fall 2010 | |
| STAT 5510 | Contemporary Topics in Statistics (1.00) |
| This course exposes students to new data types and emerging topics in statistical methodology and computation, emphasizing literacy and applied data-analysis. Topics vary by instructor. | |
| STAT 5559 | New Course in Statistics (1.00 - 4.00) |
| Offered Fall 2013 | This course provides the opportunity to offer a new topic in the subject area of statistics. |
| STAT 5980 | Applied Statistics Laboratory (1.00) |
| This course, the laboratory component of the department's applied statistics program, deals with the use of computer packages in data analysis. Enrollment in STAT 5980 is required for all students in the department's 5000-level applied statistics courses (STAT 5010, 5120, 5130, 5140, 5160, 5170, 5200). STAT 5980 may be repeated for credit provided that a student is enrolled in at least one of these 5000-level applied courses; however, no more than one unit of STAT 5980 may be taken in any semester. Corequisite: 5000-level STAT applied statistics course. Course was offered Spring 2013, Fall 2012, Spring 2012, Fall 2011, Spring 2011, Fall 2010, Spring 2010, Fall 2009 | |
| STAT 5999 | Topics in Statistics (3.00) |
| Studies topics in statistics that are not part of the regular course offerings. Prerequisite: Instructor permission. | |
| STAT 6120 | Linear Models (4.00) |
| Offered Fall 2013 | Course develops fundamental methodology to regression and linear-models analysis in general. Topics include model fitting and inference, partial and sequential testing, variable selection, transformations, diagnostics for influential observations, multicollinearity, and regression in nonstandard settings. Conceptual discussion in lectures is supplemented withhands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission. |
| STAT 6130 | Applied Multivariate Statistics (4.00) |
| This course develops fundamental methodology to the analysis of multivariate data. Topics include the multivariate normal distributions, multivariate regression, multivariate analysis of variance (MANOVA), principal components analysis, factor analysis, and discriminant analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied dataanalysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission. | |
| STAT 6190 | Introduction to Mathematical Statistics (3.00) |
| Offered Fall 2013 | This course introduces fundamental concepts in probability that underlie statistical thinking and methodology. Topics include the probability framework, canonical probability distributions, transformations, expectation, moments and momentgenerating functions, parametric families, elementary inequalities, multivariate distributions, and convergence concepts for sequences of random variables. Prerequisite:Graduate standing in Statistics, or instructor permission. |
| STAT 6250 | Longitudinal Data Analysis (4.00) |
| This course develops fundamental methodology to the analysis of longitudinal data. Topics include data structures, modeling the mean and covariance, estimation and inference with respect to the marginal models, linear mixed-effects models, and generalized linear mixed-effects models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission. | |
| STAT 6260 | Categorical Data Analysis (4.00) |
| This course develops fundamental methodology to the analysis of categorical data. Topics include contingency tables, generalized linear models, logistic regression, and logit and loglinear models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission. | |
| STAT 6440 | Introduction to Bayesian Methods (4.00) |
| Course provides an introduction to Bayesian methods with an emphasis on modeling and applications. Topics include the elicitation of prior distributions, deriving posterior and predictive distributions and their moments, Bayesian linear and generalized linear regression, and Bayesian hierarchical models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission. | |
| STAT 6510 | Advanced Data Experience (1.00) |
| This course develops skills in using data analysis to contribute to research. Each student completes a data-analysis project using data from an interdisciplinary research effort. Topics will vary, and are tailored to the objectives of the projects, and may include discussion of computationally intensive statistical methods that are commonly applied in research. | |
| STAT 6520 | Statistical literature (1.00) |
| This course develops skills in reading the statistical research literature and prepares the student for contributing to it. Each student completes a well written and properly formatted paper that would be suitable for publication. The paper reviews literature relevant to a specialized research area, and possibly suggests an original research problem. Topics will vary from term to term. | |
| STAT 7110 | Introduction to the Foundations of Statistics (3.00) |
| This course introduces fundamental concepts in the classical theory of statistical inference. Topics include sufficiency and related statistical principles, elementary decision theory, point estimation, hypothesis testing, likelihood-ratio tests, interval estimation, large-sample analysis, and elementary modeling applications. Prerequisite: Graduate standing in Statistics, or instructor permission. | |
| STAT 7120 | Statistical Inference (3.00) |
| Offered Fall 2013 | A rigorous mathematical development of the principles of statistics. Covers point and interval estimation, hypothesis testing, asymtotic theory, Bayesian statistics, and decision theory from a unified perspective. Prerequisite: STAT 7110 or instructor permission. Course was offered Spring 2012, Spring 2010 |
| STAT 7130 | Generalized Linear Models (4.00) |
| Course develops fundamental data-analysis methodology based on generalized linear models.Topics include the origins of generalized linear models, binary and polytomous data, probit analysis, logit models for proportions, log-linear models for counts, inverse polynomial models, quasi-likelihood models, & survival data models. Conceptual disc. is supplemented w/hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission. Course was offered Spring 2011 | |
| STAT 7150 | Non-Parametric Statistical Analysis (3.00) |
| Includes order statistics, distribution-free statistics, U-statistics, rank tests and estimates, asymtotic efficiency, Bahadur efficiency, M-estimates, one- and two-way layouts, multivariate location models, rank correlation, and linear models. Prerequisite: STAT 5190 and one of STAT 5120, 5130, 5140, 5160, 5170; or instructor permission. | |
| STAT 7180 | Sample Surveys (4.00) |
| This course develops fundamental methodology related to the main designs and estimation techniques used in sample surveys. Topics include simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non-response and other non-sampling errors. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission. Course was offered Fall 2010 | |
| STAT 7200 | Advanced Probability Theory for Applied Scientists (3.00) |
| The course will emphasize those techniques which are important for the applied statistician: various forms of convergence for random variables, central limit theorems, asymptotics for a transformation of a sequence of random variables, and an introduction to martingales. Prerequisite: MATH 5310 or instructor permission. | |
| STAT 7220 | Martingale Theory (3.00) |
| An introduction to martingale theory and stochastic differential equations with applications to survival analysis and sequential clinical trials. Prerequisites: STAT 7200 or MATH 7360. Course was offered Spring 2013, Spring 2011 | |
| STAT 7559 | Applied Biostatistical Data Analysis (1.00 - 4.00) |
| The objective is to help students integrate and apply statistical methods learned in other courses to real data from medial research. Students will learn to identifiy the scientific objectives of a study, and develop and implement appropriate strategies. They will present their intermediate and final results in both oral and written forms. This course will prepare the students for a future career as applied statisticians. Course was offered Spring 2013, Fall 2010 | |
| STAT 7950 | Statistical Bioinformatics in Medicine (3.00) |
| Provides an introduction to bioinformatics and discusses important topics in computational biology in medicine, particularly based on modern statistical computing approaches. Reviews state-of-the-art high-throughput biotechnologies, their applications in medicine, and analysis techniques. Requires active student participation in various discussions on the current topics in biotechnology and bioinformatics. Course was offered Fall 2011 | |
| STAT 7995 | Statistical Consulting (1.00 - 3.00) |
| Introduces the practice of statistical consultation. A combination of formal lectures, meetings with clients of the statistical consulting service, and sessions in the statistical computing laboratory. Prerequisite: Current registration in the statistics graduate program, or instructor permission. | |
| STAT 8120 | Topics in Statistics (3.00) |
| Study of topics in statistics that are currently the subject of active research. | |
| STAT 8170 | Advanced Time Series (3.00) |
| Introduces stationary stochastic processes, related limit theorems, and spectral representations. Includes an asymtotic theory for estimation in both the time and frequency domains. Prerequisite: MATH 7360, STAT 5170, or instructor permission. | |
| STAT 9120 | Statistics Seminar (3.00) |
| Offered Fall 2013 | Advanced graduate seminar in current research topics. Offerings in each semester are determined by student and faculty research interests. Course was offered Spring 2013, Fall 2012, Spring 2012, Fall 2011, Spring 2011, Fall 2010, Spring 2010, Fall 2009 |
| STAT 9993 | Directed Reading (1.00 - 9.00) |
| Offered Fall 2013 | Research into current statistical problems under faculty supervision. Course was offered Spring 2013, Fall 2012, Summer 2012, Spring 2012, Spring 2011, Fall 2010, Spring 2010, Fall 2009 |
| STAT 9998 | Non-Topical Research, Preparation for Doctoral Research (1.00 - 12.00) |
| For doctoral research, taken before a dissertation director has been selected. | |
| STAT 9999 | Non-Topical Research (1.00 - 12.00) |
| Offered Fall 2013 | For doctoral research, taken under the supervision of a dissertation director. Course was offered Spring 2013, Fall 2012, Spring 2012, Fall 2011, Spring 2011, Fall 2010, Spring 2010, Fall 2009 |