Statistics (ST)

ST 101  Statistics by Example  (3 credit hours)  

Sampling, experimental design, tables and graphs, relationships among variables, probability, estimation, hypothesis testing. Real life examples from the social, physical and life sciences, the humanities and sports. Credit not allowed if student has prior credit for another ST course

GEP Mathematical Sciences

Typically offered in Fall and Spring

ST 114  Statistical Programming  (3 credit hours)  

This is an introductory course in computer programming for statisticians using Python. Emphasis is on designing algorithms, problem solving, and forming good coding practices: methodical development of programs from specifications; documentation and style; appropriate use of control structures such as loops, of data types such as arrays; modular program organization; version control. Students will become acquainted with core statistical computational problems through examples and coding assignments, including computation of histograms, boxplots, quantiles, and least squares regression.

Restriction: Statistics majors only

Typically offered in Fall only

ST 305  Statistical Methods  (4 credit hours)  

Basic concepts of data collection, sampling, and experimental design. Descriptive analysis and graphical displays of data. Probability concepts, and expectations. Normal and binomial distributions. Sampling distributions and the Central Limit Theorem. Confidence intervals and hypothesis testing. Tests for means/proportions of two independent groups. One factor analysis of variance. Understanding relationships among variables; correlation and simple linear regression. Computer use is emphasized.

Prerequisite: MA 141; Corequisite: ST 307

Typically offered in Fall and Spring

ST 307  Introduction to Statistical Programming- SAS  (1 credit hours)  

An introduction to using the SAS statistical programming environment. The course will combine lecture and a virtual computing laboratory to teach students how to use the SAS sytem for: basic data input and manipulation; graphical displays of univariate

Corequisite: ST 305 or ST 312 or ST 372 or Prerequisite: ST 350 or BUS 350

Typically offered in Fall and Spring

ST 308  Introduction to Statistical Programming - R  (1 credit hours)  

Introduction to the statistical programming language R. The course will cover: reading and manipulating data; use of common data structures (vectors, matrices, arrays, lists); basic graphical representations.

Typically offered in Spring only

ST 311  Introduction to Statistics  (3 credit hours)  

Examining relationships between two variables using graphical techniques, simple linear regression and correlation methods. Producing data using experiment design and sampling. Elementary probability and the basic notions of statistical inference including confidence interval estimation and tests of hypothesis. One and two sample t-tests, one-way analysis of variance, inference for count data and regression. Credit not allowed if student has prior credit for another ST course or BUS 350

GEP Mathematical Sciences

Typically offered in Fall, Spring, and Summer

ST 312  Introduction to Statistics II  (3 credit hours)  

A further examination of statistics and data analysis. Inference for comparing multiple samples, experimental design, analysis of variance and post-hoc tests. Inference for correlation, simple regression, multiple regression, and curvilinear regression. Analysis of contingency tables and categorical data. No credit for students who have credit for ST 305.

Prerequisite: ST 311

GEP Mathematical Sciences

Typically offered in Fall and Spring

ST 350/BUS 350  Economics and Business Statistics  (3 credit hours)  

Introduction to statistics applied to management, accounting, and economic problems. Emphasis on statistical estimation, inference, simple and multiple regression, and analysis of variance. Use of computers to apply statistical methods to problems encountered in management and economics.

Prerequisite: MA 114

Typically offered in Fall, Spring, and Summer

ST 370  Probability and Statistics for Engineers  (3 credit hours)  

Calculus-based introduction to probability and statistics with emphasis on Monte Carlo simulation and graphical display of data on computer workstations. Statistical methods include point and interval estimation of population parameters and curve and s

Prerequisite: MA 241

Typically offered in Fall, Spring, and Summer

ST 371  Introduction to Probability and Distribution Theory  (3 credit hours)  

Basic concepts of probability and distribution theory for students in the physical sciences, computer science and engineering. Provides the background necessary to begin study of statistical estimation, inference, regression analysis, and analysis of variance.

Prerequisite: MA 241, Corequisite: MA 242

Typically offered in Fall and Spring

ST 372  Introduction to Statistical Inference and Regression  (3 credit hours)  

Statistical inference and regression analysis including theory and applications. Point and interval estimation of population parameters. Hypothesis testing including use of t, chi-square and F. Simple linear regression and correlation. Introduction to multiple regression and one-way analysis of variance.

Prerequisite: ST 371

Typically offered in Fall and Spring

ST 380  Probability and Statistics for the Physical Sciences  (3 credit hours)  

Introduction to probability models and statistics with emphasis on Monte Carlo simulation and graphical display of data on computer laboratory workstations. Statistical methods include point and interval estimation of population parameters and curveand

Prerequisite: MA 241

Typically offered in Fall only

ST 401  Experiences in Data Analysis  (4 credit hours)  

This course will allow students to see many practical aspects of data analysis. Each section of this course will expose students to the process of data analysis in a themed area such as biostatistics or environmental statistics. Students will see problems of data collection and analysis through a combination of classroom demonstrations, hands on computer activities and visits to local industries.

Prerequisite: Permission of Instructor and either ST 311 or ST 305

Typically offered in Summer only

ST 404/GPH 404  Epidemiology and Statistics in Global Public Health  (3 credit hours)  

This course will provide a general introduction to the quantitative methods used in global health, combining elements of epidemiology and biostatistics. The course will focus on linear and logistic regression, survival analysis, traditional study designs, and modern study designs. Students will learn fundamental principles in epidemiology, including statistical approaches, and apply them to topics in global public health. The course prerequisite is a B- or better in one of these courses: ST 305, ST 311, ST 350, ST 370, or ST 371. In addition, a B- or better in GPH 201 is strongly recommended.

Pre-requisite: B- or better in one of these courses: ST 305, ST 311, ST 350, ST 370, or 371

Typically offered in Fall only

ST 405/ST 505  Applied Nonparametric Statistics  (3 credit hours)  

Statistical methods requiring relatively mild assumptions about the form of the population distribution. Classical nonparametric hypothesis testing methods, Spearman and Kendall correlation coefficients, permutation tests, bootstrap methods, and nonparametric regressions will be covered.

Prerequisite: ST 508 or ST 512 or ST 514 or ST 516

Typically offered in Fall only

ST 412/MA 412  Long-Term Actuarial Models  (3 credit hours)  

Long-term probability models for risk management systems. Theory and applications of compound interest, probability distributions of failure time random variables, present value models of future contingent cash flows, applications to insurance, health care, credit risk, environmental risk, consumer behavior and warranties.

Prerequisite: MA 241 or MA 231, Corequisite: MA 421, BUS(ST) 350, ST 301, ST 305, ST 311, ST 361, ST 370, ST 371, ST 380 or equivalent

Typically offered in Fall only

ST 413/MA 413  Short-Term Actuarial Models  (3 credit hours)  

Short-term probability models for risk management systems. Frequency distributions, loss distributions, the individual risk model, the collective risk model, stochastic process models of solvency requirements, applications to insurance and businessdecisions.

Prerequisite: MA 241 or MA 231, and one of MA 421, ST 301, ST 305, ST 370, ST 371, ST 380, ST 421.

Typically offered in Summer only

ST 421  Introduction to Mathematical Statistics I  (3 credit hours)  

First of a two-semester sequence of mathematical statistics, primarily for undergraduate majors in Statistics. Introduction to probability, univariate and multivariate probability distributions and their properties, distributions of functions of random variables, random samples and sampling distributions. Credit is not allowed for both ST 421 and MA 421.

Prerequisite: MA 242

Typically offered in Fall and Spring

ST 422  Introduction to Mathematical Statistics II  (3 credit hours)  

Second of a two-semester sequence of mathematical statistics, primarily for undergraduate majors in Statistics. Random samples, point and interval estimators and their properties, methods of moments, maximum likelihood, tests of hypotheses, elements of nonparametric statistics and elements of general linear model theory.

Prerequisite: ST 421 or MA 421

Typically offered in Fall and Spring

ST 430  Introduction to Regression Analysis  (3 credit hours)  

Regression analysis as a flexible statistical problem solving methodology. Matrix review; variable selection; prediction; multicolinearity; model diagnostics; dummy variables; logistic and non-linear regression. Emphasizes use of computer.

Prerequisites: (ST 305 or ST 312 or ST 372) and ST 307 and (MA 305 or MA 405)

Typically offered in Fall and Spring

ST 431  Introduction to Experimental Design  (3 credit hours)  

Experimental design as a method for organizing analysis procedures. Completely randomized, randomized block, factorial, nested, latin squares, split-plot and incomplete block designs. Response surface and covariance adjustment procedures. Stresses use of computer.

Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307

Typically offered in Fall, Spring, and Summer

ST 432  Introduction to Survey Sampling  (3 credit hours)  

Design principles pertaining to planning and execution of a sample survey. Simple random, stratified random, systematic and one- and two-stage cluster sampling designs. Emphasis on statistical considerations in analysis of sample survey data. Class project on design and execution of an actual sample survey.

Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307

Typically offered in Fall and Spring

ST 433/ST 533  Applied Spatial Statistics  (3 credit hours)  

Introduction to statistical models and methods for analyzing various types of spatially referenced data. The focus is on applications with real data and their analysis with statistical programs such as R and SAS. Students are required to write, modify, and run computer code in order to complete homework assignments and final projects.

Typically offered in Spring only

ST 434/ST 534  Applied Time Series  (3 credit hours)  

Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. A brief review of necessary statistical concepts and R will be given at the beginning. Analyses of real data sets using the stati

Prerequisite: ST 422 and ST 430

Typically offered in Fall only

ST 435/ST 535  Statistical Methods for Quality and Productivity Improvement  (3 credit hours)  

Use of statistics for quality control and productivity improvement. Control chart calculations and graphing, process control and specification; sampling plans; and reliability. Computer use will be stressed for performing calculations and graphing.

Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307

Typically offered in Fall only

ST 437/ST 537  Applied Multivariate and Longitudinal Data Analysis  (3 credit hours)  

An introduction to use of statistical methods for analyzing multivariate and longitudinal data collected in experiments and surveys. Topics covered include multivariate analysis of variance, discriminant analysis, principal components analysis, factor analysis, covariance modeling, and mixed effects models such as growth curves and random coefficient models. Emphasis is on use of a computer to perform statistical analysis of multivariate and longitudinal data.

Prerequisite: ST 422 and ST 430

Typically offered in Spring only

ST 440/ST 540  Applied Bayesian Analysis  (3 credit hours)  

Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS); linear and hierarchical models; model selection and diagnostics.

Prerequisite: ST 422 and ST 430

Typically offered in Spring only

ST 442/CSC 442  Introduction to Data Science  (3 credit hours)  

Overview of data structures, data lifecycle, statistical inference. Data management, queries, data cleaning, data wrangling. Classification and prediction methods to include linear regression, logistic regression, k-nearest neighbors, classification and regression trees. Association analysis. Clustering methods. Emphasis on analyzing data, use and development of software tools, and comparing methods.

Prerequisite: (MA 305 or MA 405) and (ST 305 or ST 312 or ST 370 or ST 372 or ST 380) and (CSC 111 or CSC 112 or CSC 113 or CSC 114 or CSC 116 or ST 114 or ST 445)

Typically offered in Fall only

ST 445  Introduction to Statistical Computing and Data Management  (3 credit hours)  

Detailed discussion of the program data vector and data handling techniques that are required to apply statistical methods. Topics are based on the current content of the Base SAS Certification Exam and typically include: importing, validating, and exporting of data files; manipulating, subsetting, and grouping data; merging and appending data sets; basic detail and summary reporting; and code debugging. Additional topics with practical applications, such as graphics and advanced reporting, may also be introduced. Statistical methods for analyzing data are not covered in this course. Regular access to a computer for homework and class exercises is required. Previous exposure to SAS is expected.

Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307

Typically offered in Fall and Spring

ST 446  Intermediate SAS Programming with Applications  (3 credit hours)  

This course covers a wide range of SAS skills that build on the topics introduced in ST 445: Introduction to Statistical Computing and Data Management. In particular, many topics related to the Advanced SAS Certifi cation Exam are covered in order to help students prepare for that exam. However, an additional goal of equal importance is to synthesize statistical content such as regression, distributional assumptions for inference, and power from multiple courses through simulation- and graphics-based investigations.

Prerequisite: ST 430 and ST 445

Typically offered in Spring only

ST 491  Statistics in Practice  (3 credit hours)  

Mentored experience in applied statistical analysis. Students will work in small groups in collaboration with local scientists to answer real questions about real data. The experience involves mentoring by both the project scientist and the instructor.

Typically offered in Spring only

ST 495  Special Topics in Statistics  (1-6 credit hours)  

Offered as needed to present material not normally available in regular departmental course offerings, or for offering new courses on a trial basis.

Typically offered in Fall, Spring, and Summer

ST 497  Professional Experience in Statistics  (1-3 credit hours)  

Mentored professional experience in statistics. A minimum of 45 hours must be completed for each credit hour earned. The experience must be arranged in advance by the student and approved by the Department of Statistics prior to enrollment. Approval requires completion of the Statistics Department's Experiential Learning Contract, which must be signed by the student, their professional mentor, and their academic advisor. Professional mentors are encouraged to require a research paper or poster presentation as part of the work expectations when appropriate. Students should refer to their curriculum requirements for possible restrictions on the total number of ST 497 credit hours that may be applied to their degree.

Prerequisite: Sophomore Standing. Students are responsible for identifying their own internship mentor and experience.

Typically offered in Fall, Spring, and Summer

ST 498  Independent Study In Statistics  (1-6 credit hours)  

Detailed investigation of topics of particular interest to advanced undergraduates under faculty direction. Individualized/Independent Study and Research courses require a ""Course Agreement for Students Enrolled in Non-Standard Courses"" be completed

Prerequisite: Six hours of ST

Typically offered in Fall, Spring, and Summer

ST 499  Research Experience in Statistics  (1-3 credit hours)  

Mentored research experience in statistics. A minimum of 45 hours must be completed for each credit hour earned. The experience must be arranged in advance by the student and approved by the Department of Statistics prior to enrollment. Approval requires completion of the Statistics Department's Experiential Learning Contract, which must be signed by the student, their research mentor, and their academic advisor. Research mentors are encouraged to require a research paper or poster presentation as part of the work expectations when appropriate. Students should refer to their curriculum requirements for possible restrictions on the total number of ST 499 credit hours that may be applied to their degree.

Prerequisite: Sophomore Standing. Students are responsible for identifying their own research mentor and experience.

Typically offered in Fall, Spring, and Summer

ST 501  Fundamentals of Statistical Inference I  (3 credit hours)  

First of a two-semester sequence in probability and statistics taught at a calculus-based level. Probability: discrete and continuous distributions, expected values, transformations of random variables, sampling distributions. Credit not given for both ST 701 and ST 501. Note: this course will be offered in person (Fall) and online (Summer).

Prerequisite: MA 242 or equivalent

Typically offered in Fall and Summer

ST 502  Fundamentals of Statistical Inference II  (3 credit hours)  

Second of a two-semester sequence in probability and statistics taught at a calculus-based level. Statistical inference: methods of construction and evaluation of estimators, hypothesis tests, and interval estimators, including maximum likelihood. Credit not given for both ST 702 and ST 502. Note: this course will be offered in person (Spring) and online (Fall).

Prerequisite: ST 501

Typically offered in Fall and Spring

ST 503  Fundamentals of Linear Models and Regression  (3 credit hours)  

Estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markov theorem. Estimability, analysis of variance and co variance in a unified manner. Practical model-building in linear regression including residual analysis, regression diagnostics, and variable selection. Emphasis on use of the computer to apply methods with data sets. Credit not given for both ST 705 and ST 503. Note: this course will be offered in person (Spring) and online (Summer).

P: ST 501 and MA 405 or equivalent (Linear Algebra); C: ST 502

Typically offered in Spring and Summer

ST 505/ST 405  Applied Nonparametric Statistics  (3 credit hours)  

Statistical methods requiring relatively mild assumptions about the form of the population distribution. Classical nonparametric hypothesis testing methods, Spearman and Kendall correlation coefficients, permutation tests, bootstrap methods, and nonparametric regressions will be covered.

Prerequisite: ST 508 or ST 512 or ST 514 or ST 516

Typically offered in Fall only

ST 506  Sampling Animal Populations  (3 credit hours)  

Statistical methods applicable to sampling of wildlife populations, including capture-recapture, removal, change in ratio, quadrant and line transect sampling. Emphasis on model assumptions and study design.

Prerequisite: ST 512

Typically offered in Fall only

ST 507  Statistics For the Behavioral Sciences I  (3 credit hours)  

A general introduction to the use of descriptive and inferential statistics in behavioral science research. Methods for describing and summarizing data presented, followed by procedures for estimating population parameters and testing hypotheses concerning summarized data.

Prerequisite: Graduate standing

Typically offered in Fall and Spring

ST 508  Statistics For the Behavioral Sciences II  (3 credit hours)  

Introduction to use of statistical design principles in behavioral science research. Presentation of use of a statistical model to represent structure of data collected from a designed experiment or survey study. Opportunities provided for use of a computer to perform analyses of data, to evaluate proposed statistical model and to assist in post-hoc analysis procedures. Least squares principles used to integrate topics of multiple linear regression analysis, the analysis of variance and analysis of covariance.

Prerequisite: ST 507

Typically offered in Spring only

ST 511  Statistical Methods For Researchers I  (3 credit hours)  

Basic concepts of statistical models and use of samples; variation, statistical measures, distributions, tests of significance, analysis of variance and elementary experimental design, regression and correlation, chi-square.

Prerequisite: Graduate Standing

Typically offered in Fall, Spring, and Summer

ST 512  Statistical Methods For Researchers II  (3 credit hours)  

Covariance, multiple regression, curvilinear regression, concepts of experimental design, factorial experiments, confounded factorials, individual degrees of freedom and split-plot experiments. Computing laboratory addressing computational issues and use of statistical software.

Prerequisite: ST 511 or ST 513 or ST 517

Typically offered in Fall, Spring, and Summer

ST 513  Statistics for Management I  (3 credit hours)  

Analysis of data to represent facts, guide decisions and test opinions in managing systems and processes. Graphical and numerical data analysis for descriptive and predictive decisions. Scatter plot smoothing and regression analysis. Basic statistical inference. Integrated use of computer.

Prerequisite: Graduate standing

Typically offered in Fall and Spring

ST 514  Statistics For Management and Social Sciences II  (3 credit hours)  

Linear regression, multiple regression and concepts of designed experiments in an integrated approach, principles of the design and analysis of sample surveys, use of computer for analysis of data.

Prerequisite: ST 513

Typically offered in Spring and Summer

ST 515  Experimental Statistics for Engineers I  (3 credit hours)  

General statistical concepts and techniques useful to research workers in engineering, textiles, wood technology, etc. Probability distributions, measurement of precision, simple and multiple regression, tests of significance, analysis of variance,enumeration data and experimental design.

Prerequisite: Graduate standing

Typically offered in Fall and Spring

ST 516  Experimental Statistics For Engineers II  (3 credit hours)  

General statistical concepts and techniques useful to research workers in engineering, textiles, wood technology, etc. Probability distributions, measurement of precision, simple and multiple regression, tests of significance, analysis of variance, enumeration data and experimental designs.

Prerequisite: ST 515

Typically offered in Fall and Spring

ST 517  Applied Statistical Methods I  (3 credit hours)  

Course covers basic methods for summarizing and describing data, accounting for variability in data, and techniques for inference. Topics include basic exploratory data analysis, probability distributions, confidence intervals, hypothesis testing, and regression analysis. This is a calculus-based course. Statistical software is used; however, there is no lab associated with the course. Credit not given for this course and ST 511 or ST 513 or ST 515. This course does NOT count as an elective towards a degree or a minor in Statistics. Note: the course will be offered in person (Fall) and online (Fall and Summer).

Prerequisites: MA 241 or equivalent (Calculus II) and MA 405 or equivalent (Linear Algebra)

Typically offered in Fall and Summer

ST 518  Applied Statistical Methods II  (3 credit hours)  

This second course in statistics for graduate students is intended to further expand students' background in the statistical methods that will assist them in the analysis of data. Course covers many fundamental analysis methods currently used to analyze a wide array of data, mostly arising from designed experiments. Topics include multiple regression models, factorial effects models, general linear models, mixed effect models, logistic regression analysis, and basic repeated measures analysis. This is a calculus-based course. Statistical software is used, however, there is no lab associated with the course. Credit not given for this course and ST 512 or ST 514 or ST 516. Note: this course will be offered in person (Spring) and online (Fall and Spring).

Prerequisite: ST 517

Typically offered in Fall and Spring

ST 519/EMS 519  Teaching and Learning of Statistical Thinking  (3 credit hours)  

This course is designed to bridge theory and practice on how students develop understandings of key concepts in data analysis, statistics, and probability. Discussion of students' understandings, teaching strategies and the use of manipulatives and technology tools. Topics include distribution, measures of center and spread, sampling, sampling distribution, randomness, and law of large numbers. Must complete a first level graduate statistics course ( ST 507, ST 511, or equivalent) before enrolling.

Prerequisite: ST 507 or ST 511

Typically offered in Spring only

ST 520  Statistical Principles of Clinical Trials  (3 credit hours)  

Statistical methods for design and analysis of clinical trials and epidemiological studies. Phase I, II, and III clinical trials. Principle of Intention-to-Treat, effects of non-compliance, drop-outs. Interim monitoring of clinical trials and data safety monitoring boards. Introduction to meta-analysis. There is also discussion of Epidemiological methods time permitting.

Corequisite: ST 501 or ST 521 or ST 701

Typically offered in Fall only

ST 524  Statistics In Plant Science  (3 credit hours)  

Principles and techniques of planning, establishing and executing field and greenhouse experiments. Size, shape and orientation of plots; border effects; estimation of size of experiments for specified accuracy; subsampling plots and yields for laboratory analysis; combining data from a series of years and/or locations; rotation experiments; repeated measures data; multiple comparisons in variety trial results; selection of predictors in multiple regression; introduction to interspecies and intraspecies plant competition experiments and models.

Prerequisite: ST 512

Typically offered in Fall only

ST 531  Experimental Design  (3 credit hours)  

Overview and comparison of observational studies and designed experiments followed by a thorough discussion of design principles. Review of estimation and inference for regression and ANOVA models from an experimental design perspective. Review of design and analysis for completely randomized, randomized complete block, and Latin square designs. Designs and analysis methods for factorial experiments, general blocking structures, incomplete block designs, confounded factorials, split-plot experiments, and fractional factorial designs. Examples used to illustrate application and analysis of these designs.

Prerequisite: ST 512, or ST 515, or ST 516, or ST 517, or ST 703

Typically offered in Fall only

ST 533/ST 433  Applied Spatial Statistics  (3 credit hours)  

Introduction to statistical models and methods for analyzing various types of spatially referenced data. The focus is on applications with real data and their analysis with statistical programs such as R and SAS. Students are required to write, modify, and run computer code in order to complete homework assignments and final projects.

Typically offered in Spring only

ST 534/ST 434  Applied Time Series  (3 credit hours)  

Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. A brief review of necessary statistical concepts and R will be given at the beginning. Analyses of real data sets using the stati

Prerequisite: ST 422 and ST 430

Typically offered in Fall only

ST 535/ST 435  Statistical Methods for Quality and Productivity Improvement  (3 credit hours)  

Use of statistics for quality control and productivity improvement. Control chart calculations and graphing, process control and specification; sampling plans; and reliability. Computer use will be stressed for performing calculations and graphing.

Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307

Typically offered in Fall only

ST 537/ST 437  Applied Multivariate and Longitudinal Data Analysis  (3 credit hours)  

An introduction to use of statistical methods for analyzing multivariate and longitudinal data collected in experiments and surveys. Topics covered include multivariate analysis of variance, discriminant analysis, principal components analysis, factor analysis, covariance modeling, and mixed effects models such as growth curves and random coefficient models. Emphasis is on use of a computer to perform statistical analysis of multivariate and longitudinal data.

Prerequisite: ST 422 and ST 430

Typically offered in Spring only

ST 540/ST 440  Applied Bayesian Analysis  (3 credit hours)  

Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS); linear and hierarchical models; model selection and diagnostics.

Prerequisite: ST 422 and ST 430

Typically offered in Spring only

ST 542  Statistical Practice  (3 credit hours)  

This course will provide a discussion-based introduction to statistical practice geared towards students in the final semester of their Master of Statistics degree. Note: the course will be offered in person (Fall) and online (Spring and Summer).

Typically offered in Fall, Spring, and Summer

ST 544  Applied Categorical Data Analysis  (3 credit hours)  

This course focuses on the concepts, methods, and models used to analyze categorical data, particularly contingency tables, count data and binary/binomial type of data. The topics covered include Pearson Chi-squared independence test for contingency tables, measures of marginal and conditional associations, small-sample inference, logistic regression models for independent binary/binomial data and many extended models for correlated binary/binomial data including matched data and longitudinal data. The course emphasizes the implementation of methods/models using SAS and the interpretation of the results from the output.

Prerequisite: ST 512 or ST 514 or ST 515 or ST 516

Typically offered in Fall only

ST 546/MA 546  Probability and Stochastic Processes I  (3 credit hours)  

Modern introduction to Probability Theory and Stochastic Processes. The choice of material is motivated by applications to problems such as queueing networks, filtering and financial mathematics. Topics include: review of discrete probability and continuous random variables, random walks, markov chains, martingales, stopping times, erodicity, conditional expectations, continuous-time Markov chains, laws of large numbers, central limit theorem and large deviations.

Prerequisite: MA 421 and MA 425 or MA 511

Typically offered in Fall only

ST 555  Statistical Programming I  (3 credit hours)  

An introduction to programming and data management using SAS, the industry standard for statistical practice. Detailed discussion of the program data vector and data handling techniques that are required to apply statistical methods. Topics are based on the current content of the Base SAS Certification Exam and typically include: importing, validating, and exporting of data files; manipulating, subsetting, and grouping data; merging and appending data sets; basic detail and summary reporting; and code debugging. Additional topics with practical applications are also introduced, such as graphics and advanced reporting. Statistical methods for analyzing data are not covered in this course. Regular access to a computer for homework and class exercises is required. Previous exposure to SAS is not expected.

Prerequisite: Graduate standing

Typically offered in Fall, Spring, and Summer

ST 556  Statistical Programming II  (3 credit hours)  

Statistical procedures for importing/managing complex data structures using SQL, automated analysis using macro programming, basic simulation methods and text parsing/analysis procedures. Students learn SAS, the industry standard for statistical practice. Regular access to a computer for homework and class exercises is required.

P: ST 555 or Base SAS Certification

Typically offered in Spring and Summer

ST 557  Using Technology to Teach Statistics  (3 credit hours)  

This course will provide statistics educators with an in-depth introduction to applying technology for teaching college statistics. In this course, students will explore a variety of available statistical packages, demonstration applets, and other technologies for teaching statistics. Students will learn pedagogy t help them structure learning activities around these technologies. Students will also learn to identify key elements in technologies that support pedagogical goals.

Typically offered in Fall only

ST 558  Data Science for Statisticians  (3 credit hours)  

Methods for reading, manipulating, and combining data sources including databases. Custom functions, visualizations, and summaries. Common analyses done by data scientists. Methods for communicating results including dashboards. Regular access to a computer for homework and class exercises is required.

Prerequisites: (ST 511 or ST 517 or equivalent) and (ST 555 or equivalent)

Typically offered in Fall and Summer

ST 561/ECG 561  Applied Econometrics I  (3 credit hours)  

Introduction and application of econometrics methods for analyzing cross-sectional data in economics, and other social science disciplines, such as OLS, IV regressions, and simultaneous equations models. Students should have had a statistical methods course at the 300 level or above as well as Calculus I and II.

Typically offered in Fall only

ST 562  Data Mining with SAS Enterprise Miner  (3 credit hours)  

This is a hands-on course using modeling techniques designed mostly for large observational studies. Estimation topics include recursive splitting, ordinary and logistic regression, neural networks, and discriminant analysis. Clustering and association analysis are covered under the topic "unsupervised learning," and the use of training and validation data sets is emphasized. Model evaluation alternatives to statistical significance include lift charts and receiver operating characteristic curves. SAS Enterprise Miner is used in the demonstrations, and some knowledge of basic SAS programming is helpful.

Prerequisite: ST 512 or ST 514 or ST 515 or ST 516 or ST 517

Typically offered in Spring only

ST 563  Introduction to Statistical Learning  (3 credit hours)  

This course will introduce common statistical learning methods for supervised and unsupervised predictive learning in both the regression and classification settings. Topics covered will include linear and polynomial regression, logistic regression and discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, principal components, hierarchical clustering, nearest neighbor, kernel, and tree-based methods, ensemble methods, boosting, and support-vector machines.

Prerequisite: ST 512 or ST 514 or ST 515 or ST 517

Typically offered in Summer only

ST 590  Special Topics  (1-6 credit hours)  

Typically offered in Fall, Spring, and Summer

ST 601  Seminar  (1 credit hours)  

Typically offered in Fall, Spring, and Summer

ST 610  Topics in Stat  (1-6 credit hours)  

Special topics in Statistics.

ST 630  Independent Study  (1-3 credit hours)  

Typically offered in Fall, Spring, and Summer

ST 635  Readings  (1-3 credit hours)  

Typically offered in Spring only

ST 641  Statistical Consulting  (1 credit hours)  

Participation in regularly scheduled supervised statistical consulting sessions with faculty member and client. Consultant's report written for each session. Regularly scheduled meetings with course instructor and other student consultants to present an

Prerequisite: ST 512 and ST 702

Typically offered in Fall, Spring, and Summer

ST 685  Master's Supervised Teaching  (1-3 credit hours)  

Teaching experience under the mentorship of faculty who assist the student in planning for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment.

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

ST 690  Master's Examination  (1-9 credit hours)  

For students in non thesis master's programs who have completed all other requirements of the degree except preparing for and taking the final master's exam.

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

ST 693  Master's Supervised Research  (1-9 credit hours)  

Instruction in research and research under the mentorship of a member of the Graduate Faculty.

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

ST 695  Master's Thesis Research  (1-9 credit hours)  

Thesis Research

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

ST 696  Summer Thesis Research  (1 credit hours)  

For graduate students whose programs of work specify no formal course work during a summer session and who will be devoting full time to thesis research.

Prerequisite: Master's student

Typically offered in Summer only

ST 699  Master's Thesis Preparation  (1-9 credit hours)  

For students who have completed all credit hour requirements and full-time enrollment for the master's degree and are writing and defending their thesis. Credits Arranged

Prerequisite: Master's student

Typically offered in Fall, Spring, and Summer

ST 701  Statistical Theory I  (3 credit hours)  

Probability tools for statistics: description of discrete and absolutely continuous distributions, expected values, moments, moment generating functions, transformation of random variables, marginal and conditional distributions, independence, orderstatistics, multivariate distributions, concept of random sample, derivation of many sampling distributions.

Typically offered in Fall only

ST 702  Statistical Theory II  (3 credit hours)  

General framework for statistical inference. Point estimators: biased and unbiased, minimum variance unbiased, least mean square error, maximum likelihood and least squares, asymptotic properties. Interval estimators and tests of hypotheses: confidence intervals, power functions, Neyman-Pearson lemma, likelihood ratio tests, unbiasedness, efficiency and sufficiency.

Prerequisite: ST 701

Typically offered in Spring only

ST 703  Statistical Methods I  (3 credit hours)  

Introduction of statistical methods. Examples include multiple linear regression, concepts of experimental design, factorial experiments, and random-effects modeling. A computing laboratory addresses computational issues and use of statistical software. This course is a prerequisite for most advanced courses in statistics. This section is restricted to statistics and closely related majors.

R: 17STPHD Students Only

Typically offered in Fall only

ST 704  Statistical Methods II  (3 credit hours)  

This course will introduce many methods that are commonly used in applications. Examples include: model generation, selection, assessment, and diagnostics in the context of multiple linear regression (including penalized regression); linear mixed models; generalized linear models; generalized linear mixed models; nonparametric regression and smoothing; and finite-population sampling basics. Coverage will include some theory, plus implementation using SAS and/or R.

Prerequisite:ST 703; Corequisites: ST 702 and ST 705

Typically offered in Spring only

ST 705  Linear Models and Variance Components  (3 credit hours)  

Theory of estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markoff theorem. Estimability and properties of best linear unbiased estimators. General linear hypo

Corequisite: ST 702

Typically offered in Spring only

ST 706/MA 706/OR 706  Nonlinear Programming  (3 credit hours)  

An advanced mathematical treatment of analytical and algorithmic aspects of finite dimensional nonlinear programming. Including an examination of structure and effectiveness of computational methods for unconstrained and constrained minimization. Specia

Prerequisite: OR(IE,MA) 505 and MA 425

Typically offered in Spring only

ST 708  Applied Least Squares  (3 credit hours)  

Least squares estimation and hypothesis testing procedures for linear models. Consideration of regression, analysis of variance and covariance in a unified manner. Emphasis on use of the computer to apply these techniques to experimental (including unequal cell sizes) and survey situations.

Prerequisite: ST 512

Typically offered in Fall only

ST 711  Design Of Experiments  (3 credit hours)  

Review of completely randomized, randomized complete block and Latin square designs and basic concepts in the techniques of experimental design. Designs and analysis methods in factorial experiments, confounded factorials, response surface methodology, change-over design, split-plot experiments and incomplete block designs. Examples used to illustrate application and analysis of these designs.

Prerequisite: ST 512 or ST 516

Typically offered in Fall only

ST 715  Theory Of Sampling Applied To Survey Design  (3 credit hours)  

Principles for interpretation and design of sample surveys. Estimator biases, variances and comparative costs. Simple random sample, cluster sample, ratio estimation, stratification, varying probabilities of selection. Multi-stage, systematic and double sampling. Response errors.

Prerequisite: ST 422, ST 512

Typically offered in Fall only

ST 721/GN 721  Genetic Data Analysis  (3 credit hours)  

Analysis of discrete data, illustrated with genetic data on morphological characters allozymes, restriction fragment length polymorphisms and DNA sequences. Maximum likelihood estimation, including iterative procedures. Numerical resampling. Development of statistical techniques for characterizing genetic disequilibrium and diversity. Measures of population structure and genetic distance. Construction of phylogenetic trees. Finding alignments and similarities between DNA sequences. Locating genes with markers.

Prerequisite: ST 430 and GN 311

Typically offered in Spring only

ST 730  Applied Time Series Analysis  (3 credit hours)  

An introduction to use of statistical methods for analyzing and forecasting data observed over time. Trigonometric regression, periodogram/spectral analysis. Smoothing. Autoregressive moving average models. Regression with autocorrelated errors. Linear filters and bivariate spectral analysis. Stress on methods and applications; software implementations described and used in assignments.

Prerequisite: ST 512

Typically offered in Fall only

ST 732  Longitudinal Data Analysis  (3 credit hours)  

Introduction to modeling longitudinal data; Population-averaged vs. subject-specific modeling; Classical repeated measures analysis of variance methods and drawbacks; Review of estimating equations; Population-averaged linear models; Linear mixed effects models; Maximum likelihood, restricted maximum likelihood, and large sample theory; Review of nonlinear and generalized linear regression models; Population-averaged models and generalized estimating equations; Nonlinear and generalized linear mixed effects models; Implications of missing data; Advanced topics (including Bayesian framework, complex nonlinear models, multi-level hierarchical models, relaxing assumptions on random effects in mixed effects models, among others). Implementation in SAS and R.

Prerequisites: ST 702 and ST 705

Typically offered in Spring only

ST 733  Spatial Statistics  (3 credit hours)  

Introduction to the theory and methods of spatial data analysis including: visualization; Gaussian processes; spectral representation; variograms; kriging; computationally-efficient methods; nonstationary processes; spatiotemporal and multivariate models.

Prerequisite: ST 705

Typically offered in Spring only

ST 740  Bayesian Inference and Analysis  (3 credit hours)  

Introduction to Bayesian inference; specifying prior distributions; conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality. Markov Chain Monte Carlo (MCMC) methods and the use of exising software(e.g., WinBUGS).

Prerequisite: ST 702

Typically offered in Fall only

ST 744  Categorical Data Analysis  (3 credit hours)  

Statistical models and methods for categorical responses including the analysis of contingency tables, logistic and Poisson regression, and generalized linear models. Survey of asymptotic and exact methods and their implementation using standard statistical software.

Prerequisite: ST 512 and ST 702

Typically offered in Spring only

ST 745  Analysis of Survival Data  (3 credit hours)  

Statistical methods for analysis of time-to-event data, with application to situations with data subject to right-censoring and staggered entry, including clinical trials. Survival distribution and hazard rate; Kaplan-Meier estimator for survival distribution and Greenwood's formula; log-rank and weighted long-rank tests; design issues in clinical trials. Regression models, including accelerated failure time and proportional hazards; partial likelihood; diagnostics.

Prerequisite: ST 502 or ST 702

Typically offered in Spring only

ST 746/MA 746  Introduction To Stochastic Processes  (3 credit hours)  

Markov chains and Markov processes, Poisson process, birth and death processes, queuing theory, renewal theory, stationary processes, Brownian motion.

Prerequisite: MA 405 and MA(ST) 546 or ST 521

Typically offered in Spring only

ST 747/MA 747  Probability and Stochastic Processes II  (3 credit hours)  

Fundamental mathematical results of probabilistic measure theory needed for advanced applications in stochastic processes. Probability measures, sigma-algebras, random variables, Lebesgue integration, expectation and conditional expectations w.r.t.sigma algebras, characteristic functions, notions of convergence of sequences of random variables, weak convergence of measures, Gaussian systems, Poisson processes, mixing properties, discrete-time martingales, continuous-time markov chains.

Prerequisite: MA(ST) 546

Typically offered in Spring only

ST 748/MA 748  Stochastic Differential Equations  (3 credit hours)  

Theory of stochastic differential equations driven by Brownian motions. Current techniques in filtering and financial mathematics. Construction and properties of Brownian motion, wiener measure, Ito's integrals, martingale representation theorem, stochastic differential equations and diffusion processes, Girsanov's theorem, relation to partial differential equations, the Feynman-Kac formula.

Prerequisite: MA(ST) 747

Typically offered in Fall only

ST 750/ECG 750  Introduction to Econometric Methods  (3 credit hours)  

Introduction to principles of estimation of linear regression models, such as ordinary least squares and generalized least squares. Extensions to time series and panel data. Consideration of endogeneity and instrumental variables estimation. Limited dependent variable and sample selection models. Attention to implementation of econometric methods using a statistical package and microeconomic and macroeconomic data sets.

Prerequisite: ST 421; Corequisite: ST 422

Typically offered in Spring only

ST 751/ECG 751  Econometric Methods  (3 credit hours)  

Introduction to important econometric methods of estimation such as Least Squares, instrumentatl Variables, Maximum Likelihood, and Generalized Method of Moments and their application to the estimation of linear models for cross-sectional ecomomic data. Discussion of important concepts in the asymptotic statistical analysis of vector process with application to the inference procedures based on the aforementioned estimation methods.

Prerequisite: ST 421, ST 422

Typically offered in Fall only

ST 752/ECG 752  Time Series Econometrics  (3 credit hours)  

The characteristics of macroeconomic and financial time series data. Discussion of stationarity and non-stationarity as they relate to economic time series. Linear models for stationary economic time series: autoregressive moving average (ARMA) models;

Prerequisite: ECG(ST) 751

Typically offered in Spring only

ST 753/ECG 753  Microeconometrics  (3 credit hours)  

The characteristics of microeconomic data. Limited dependent variable models for cross-sectional microeconomic data: logit/probit models; tobit models; methods for accounting for sample selection; count data models; duration analysis; non-parametricmeth

Prerequisite: ECG 751

Typically offered in Spring only

ST 755  Advanced Analysis Of Variance and Variance Components  (3 credit hours)  

Expected mean squares, exact and approximate tests of hypotheses for balanced and unbalanced data sets. Fixed, mixed and random models. Randomization theory. Estimation of variance components using regression, MINQUE and general quadratic unbiased estimation theory.

Prerequisite: ST 512, ST 552

Typically offered in Spring only

ST 756/GN 756  Computational Molecular Evolution  (3 credit hours)  

Phylogenetic analyses of nucleotide and protein sequence data. Sequence alignment, phylogeny reconstruction and relevant computer software. Prediction of protein secondary structure, database searching, bioinformatics and related topics. Project required.

Prerequisite: GN 311 and ST 511

Typically offered in Fall only

ST 757/HS 757/GN 757  Quantitative Genetics Theory and Methods  (3 credit hours)  

The essence of quantitative genetics is to study multiple genes and their relationship to phenotypes. How to study and interpret the relationship between phenotypes and whole genome genotypes in a cohesive framework is the focus of this course. We discuss how to use genomic tools to map quantitative trait loci, how to study epistasis, how to study genetic correlations and genotype-by-environment interactions. We put special emphasis in using genomic data to study and interpret general biological problems, such as adaptation and heterosis. The course is targeted for advanced graduate students interested in using genomic information to study a variety of problems in quantitative genetics.

Prerequisite: ST 511

Typically offered in Fall only

ST 758  Computation for Statistical Research  (3 credit hours)  

Computational tools for research in statistics, including applications of numerical linear algebra, optimization and random number generation, using the statistical language R. A project encompassing a simulation experiment will be required.

Prerequisite: ST 702 and ST 705

Typically offered in Fall only

ST 771/BMA 771/MA 771  Biomathematics I  (3 credit hours)  

Role of theory construction and model building in development of experimental science. Historical development of mathematical theories and models for growth of one-species populations (logistic and off-shoots), including considerations of age distributions (matrix models, Leslie and Lopez; continuous theory, renewal equation). Some of the more elementary theories on the growth of organisms (von Bertalanffy and others; allometric theories; cultures grown in a chemostat). Mathematical theories oftwo and more species systems (predator-prey, competition, symbosis; leading up to present-day research) and discussion of some similar models for chemical kinetics. Much emphasis on scrutiny of biological concepts as well as of mathematical structureof models in order to uncover both weak and strong points of models discussed. Mathematical treatment of differential equations in models stressing qualitative and graphical aspects, as well as certain aspects of discretization. Difference equation models.

Prerequisite: Advanced calculus, reasonable background in biology

Typically offered in Fall only

ST 772/BMA 772/MA 772  Biomathematics II  (3 credit hours)  

Continuation of topics of BMA 771. Some more advanced mathematical techniques concerning nonlinear differential equations of types encountered in BMA 771: several concepts of stability, asymptotic directions, Liapunov functions; different time-scales. Comparison of deterministic and stochastic models for several biological problems including birth and death processes. Discussion of various other applications of mathematics to biology, some recent research.

Prerequisite: BMA 771, elementary probability theory

Typically offered in Spring only

ST 773/BMA 773/MA 773/OR 773  Stochastic Modeling  (3 credit hours)  

Survey of modeling approaches and analysis methods for data from continuous state random processes. Emphasis on differential and difference equations with noisy input. Doob-Meyer decomposition of process into its signal and noise components. Examples from biological and physical sciences, and engineering. Student project.

Prerequisite: BMA 772 or ST (MA) 746

Typically offered in Spring only

ST 779  Advanced Probability for Statistical Inference  (3 credit hours)  

Sets and classes, sigma-fields and related structures, probability measures and extensions, random variables, expectation and integration, uniform integrability, inequalities, L_p-spaces, product spaces, independence, zero-one laws, convergence notions, characteristic functions, simplest limit theorems, absolute continuity, conditional expectation and conditional probabilities, martingales.

Prerequisite: ST 702

Typically offered in Fall only

ST 784  Multivariate Analysis  (3 credit hours)  

Survey of multivariate statistical theory. Multivariate distributions including the multinormal, Wishart, Hotelling's T, Fisher-Roy-Hsu, Wilks' and multivariate Beta distributions. Applications of maximum likelihood estimation, likelihood ratio testing and the union-intersection principle. Development of the theory of Hotelling's T tests and confidence sets, discriminant analysis, canonical correlation, multivariate analysis of variance and principal components.

Prerequisite: ST 522

Typically offered in Spring only

ST 790  Advanced Special Topics  (1-6 credit hours)  

Typically offered in Fall, Spring, and Summer

ST 793  Advanced Statistical Inference  (3 credit hours)  

Statistical inference with emphasis on the use of statistical models, construction and use of likelihoods, general estimating equations, and large sample methods. Includes introduction to Bayesian statistics and the jackknife and bootstrap.

Prerequisite: ST 702

Typically offered in Spring only

ST 801  Seminar  (1 credit hours)  

Typically offered in Fall and Spring

ST 810  Advanced Topics in Statistics  (1-3 credit hours)  

Typically offered in Fall and Spring

ST 830  Independent Study  (1-3 credit hours)  

Typically offered in Fall and Spring

ST 835  Readings  (1-3 credit hours)  

Typically offered in Spring only

ST 841  Statistical Consulting  (1 credit hours)  

Participation in regularly scheduled supervised statistical consulting sessions with faculty member and client. Consultant's report written for each session. Regularly scheduled meetings with course instructor and other student consultants to present an

Prerequisite: ST 512 and ST 702

Typically offered in Fall only

ST 885  Doctoral Supervised Teaching  (1-3 credit hours)  

Teaching experience under the mentorship of faculty who assist the student in planing for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

ST 890  Doctoral Preliminary Examination  (1-9 credit hours)  

For students who are preparing for and taking written and/or oral preliminary exams.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

ST 893  Doctoral Supervised Research  (1-9 credit hours)  

Instruction in research and research under the mentorship of a member of the Graduate Faculty.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

ST 895  Doctoral Dissertation Research  (1-9 credit hours)  

Dissertation Research

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer

ST 896  Summer Dissertation Research  (1 credit hours)  

For graduate students whose programs of work specify no formal course work during a summer session and who will be devoting full time to thesis research.

Prerequisite: Doctoral student

Typically offered in Summer only

ST 899  Doctoral Dissertation Preparation  (1-9 credit hours)  

For students who have completed all credit hour requirements, full-time enrollment, preliminary examination, and residency requirements for the doctoral degree, and are writing and defending their dissertations.

Prerequisite: Doctoral student

Typically offered in Fall, Spring, and Summer