Graduate Department of Statistics

College of Arts and Sciences

Chair: Eric Chicken; Director, Undergraduate Studies and Statistical Consulting Center: Ramsier; Director, Graduate Studies: Barbu; Director, Statistical Computing: Barbu; Director, Biostatistics: Sinha; Director, Statistical Data Science: Slate; Director, Statistics: She; Professors: Barbu, Cao, Chicken, Mai, Niu, Patrangenaru, She, Sinha, Slate, Srivastava, Wu, J. Zhang, X. Zhang; Associate Professors: Bradley; Assistant Professors: Barrientos, Bhattacharya, Huang, Jauch, Lin, Liu, Loyal, Stewart, Tong, Xie; Teaching Professor: Ramsier; Senior Lecturers: Bose, Shows; Professors Emeriti: Hollander, Lin, McGee, Meeter, Sethuraman

The Department of Statistics offers programs leading to the Master of Science (MS) in statistics, the Master of Science in statistics with a major in Statistical Data Science, the Master of Science (MS) in biostatistics, and the Doctor of Philosophy (PhD) degrees in statistics and biostatistics. The option of an interdisciplinary degree in Data Science with an emphasis in Statistics is also available. The MS and PhD programs prepare students for professional careers in academia, industry, and government.

The Department of Statistics also offers a graduate certificate in data analysis and SAS programming. The certificate is earned by completing specific course requirements (See https://sas.stat.fsu.edu/ for details).

Facilities

The Department of Statistics provides statistical consultation on research through the Statistical Consulting Center. The center works cooperatively with faculty and graduate students throughout FSU in research and plays a role with research teams in the design of experiments and the analysis of data. Graduate students who anticipate theses and dissertations involving statistical analyses should plan their programs to include basic training in statistics in order to take full advantage of the services of the center.

The Department of Statistics provides facilities for computation in connection with coursework and research. The Department has a local area network of workstations and PCs running Linux and Windows operating systems, as well as networked printers. Linked to the campus-wide network, these workstations may be used to access the University-operated clusters for computationally intensive projects.

Faculty members of the Department of Statistics are engaged in basic research supported by grants and contracts with such agencies as the National Science Foundation, the National Institutes of Health, and the Department of Defense.

College Requirements

Please review all college-wide degree requirements summarized in the “College of Arts and Sciences” chapter of this Graduate Bulletin.

Admission Requirements

Prior work in statistics is not a requirement for admission to graduate study. Applicants must have at least a 3.0 GPA on a 4.0 scale and have completed a three- or four-semester calculus sequence. A course in linear algebra is required and a sequence of real analysis courses is desirable but not required. Individual programs of study are developed in consultation with the departmental faculty through supervisory committees appointed during the first semester of graduate study.

Master of Science Degree

The following options for the Master of Science degree are possible:

A three-semester program emphasizing data science jointly with the Departments of Computer Science, Mathematics, and Scientific Computing, which results in an MS degree in Interdisciplinary Data Science with a major in statistics;
A three-semester program emphasizing statistical data science, which results in an MS in statistics with major in statistical data science;
A four-semester program emphasizing mathematical statistics, which results in an MS in statistics;
A four-semester program emphasizing applied statistics, which results in an MS in statistics;
A four-semester program emphasizing biostatistics, which results in an MS in biostatistics;
Undergraduates may enroll in a combined bachelor's/master's pathway. The graduate degree earned is an MS in Statistics, Statistical Data Science, or Biostatistics.

The MS in Statistics with a major in statistical data science requires thirty-two credit hours. All of the other Master of Science degrees require thirty-six credit hours, thirty of which must be taken for a letter grade. No examination is required for the MS degree but the student must meet all University academic standards. Full course programs are prepared in consultation with the student's supervisory committee. A detailed description of the Master of Science programs can be obtained on the department's Website at https://stat.fsu.edu/.

MS in Interdisciplinary Data Science

This is an interdisciplinary degree offered by the College of Arts & Science with concentrations in Computer Science, Mathematics, Scientific Computing, and Statistics. For more details, see https://ds.fsu.edu and the entry for Interdisciplinary Master's Degree in Data Science in this Bulletin.

Doctor of Philosophy Degree

The Department of Statistics offers two doctoral degrees: the PhD in Statistics and the PhD in Biostatistics.

The required courses for the PhD in biostatistics include courses that emphasize the theory, development, and application of biostatistical and computational statistics methods. The PhD in statistics includes courses that emphasize the theory and development of statistical methods.

For both degrees, course programs and exact degree requirements are determined individually for students through consultation with their supervisory committee. Both degrees require the student to achieve a firm foundation in the theory of statistics and include a PhD qualifying examination, usually taken at the beginning of the Spring semester of their second year of attendance. Both degrees also require a prospectus examination, usually conducted during their third academic year in the program. A more complete description of the degree requirements may be found on the Department of Statistics webpage at https://stat.fsu.edu.

Definition of Prefix

STA—Statistics

Graduate Courses

Note: Prerequisites are stated by number from the above list of FSU courses. The equivalent course at another institution as agreed by or consent of the instructor is sufficient.

STA 5066. Data Management and Analysis with SAS (3). Prerequisite: Previous background in statistics at least through linear regression or instructor permission. This course introduces SAS software in lab-based format. SAS is the world's most widely used statistical package for managing and analyzing data. The objective of this course is for students to develop the skills necessary to address data management and analysis issues using SAS. This course includes a complete introduction to data management for scientific and industrial data and an overview of SAS statistical procedures.

STA 5067. Advanced Data Management and Analysis with SAS (3). Prerequisite: STA 5066. This course presents additional methods for managing and analyzing data with the SAS system. It covers as many of the following topics as time permits: Advanced Data step Topics, Manipulation of Data with Proc SQL, the SAS Macro Facility, Simulation with the data step and Analyses with Proc IML.

STA 5106. Computational Methods in Statistics I (3). Prerequisites: At least one previous course in statistics above STA 1013 and some previous programming experience; or instructor permission. This course utilizes Matlab and a programming language (C/Fortran). Floating point arithmetic, numerical matrix analysis, multiple regression analysis, nonlinear optimization, root finding, numerical integration, and Monte Carlo sampling.

STA 5107. Computational Methods in Statistics II (3). Prerequisite: STA 5106 or instructor permission. This course utilizes Matlab and a programming language (C/Fortran). The course is a continuation of STA 5106 in computational techniques for linear and nonlinear statistics. The course also covers statistical image understanding, elements of pattern theory, simulated annealing, Metropolis-Hastings algorithm, and Gibbs sampling.

STA 5126. Introduction to Applied Statistics. (3). Prerequisite: MAC 1105. This course offers graduate credit for non-statistics majors. Topics include data collection, sample variation, basic probability, confidence intervals, hypothesis testing, analysis of variance, contingency tables, correlation, regression, and nonparametric statistics. No credit is given for STA 5126 if a “C-” or better is earned in STA 2023, STA 2122, STA 2171, STA 3014, STA 3032, or QMB 3200.

STA 5166. Statistics in Applications I (3). Prerequisite: MAC 2313. This course introduces topics such as comparison of two treatments, random sampling, randomization and blocking with two comparisons, statistical inference for means, variances, proportions and frequencies, and analysis of variance.

STA 5167. Statistics in Applications II (3). Prerequisite: STA 5166. This course focuses on topics such as special designs in analysis of variance, linear and nonlinear regression, least squares and weighted least squares, case analysis, model building, nonleast squares estimation.

STA 5168. Statistics in Applications III (3). Prerequisite: STA 5167. This course focuses on topics such as response surface methods, repeated measures and split-plot designs, basic log-linear and logit models for two-way and multiway tables, and multinomial response models.

STA 5172. Fundamentals of Biostatistics (3). Prerequisite: A previous course in statistics or instructor permission. This course introduces students to the statistical methods used in studying the prevention of disease in human populations.

STA 5179. Applied Survival Analysis (3). Prerequisite: STA 2171. This course is an applied introduction to survival analysis, one of the most commonly used analytic tools in biomedical studies. Topics to be covered include censoring and time scale, descriptive methods, parametric methods, and regression methods, which stress the proportional hazards model.

STA 5197. Longitudinal Data Analysis (3). Prerequisite: STA 5326. This course explores modeling longitudinal data through analysis and interpretation of the data using standard statistical software (SAS/R and WinBugs/JAGS).

STA 5198. Epidemiology for Statisticians (3). Prerequisites: STA 5167 and STA 5327 or instructor permission. This course covers fundamental methods of epidemiology for statisticians. With a focus on identification of risk factors for disease, topics include exposure-disease association, design of cohort, matched and randomized studies; cross-sectional and longitudinal studies; statistical analysis of data arising from such studies, confounding, adjustment and causality; and evaluation of diagnostic and screening tests.

STA 5206. Analysis of Variance and Design of Experiments (3). Prerequisite: One of STA 2122, STA 4322, or STA 5126. This course expounds on topics such as one and two-way classifications, nesting, blocking, multiple comparisons, incomplete designs, variance components, factorial designs, confounding. Graduate credit for non-statistics majors only.

STA 5207. Applied Regression Methods (3). Prerequisite: One of STA 2122, STA 4322, or STA 5126. This course discusses topics such as general linear hypothesis, analysis of covariance, multiple correlation and regression, response surface methods. Graduate credit for non-statistics majors only.

STA 5208. Linear Statistical Models (3). Prerequisite: STA 5327. This course covers the theoretical foundations of linear and generalized linear statistical models. It is designed for PhD students in statistics, biostatistics, and related fields (data science, computer science, electrical engineering, etc.).

STA 5225. Sample Surveys (3). Prerequisite: A course in statistics above STA 1013 or instructor permission. This course introduces topics such as simple, stratified, systematic, and cluster random sampling, ratio and regression estimation and multistage sampling.

STA 5238. Applied Logistic Regression (3). Prerequisite: STA 3032 or an equivalent upper division course that covers basic statistics at least through linear regression. This course is an applied introduction to logistic regression, one of the most commonly used analytic tools in statistical studies. Topics include fitting the model, interpretation of the model, model building, assessing model fit, model validation, and model uncertainty.

STA 5244. Clinical Trials (3). Prerequisite: STA 2171. This course offers an introduction to clinical trials. Topics to be covered include defining the research question, basic study designs, randomization, blinding, sample size, baseline assessment, data collection and quality control, monitoring, issues in data analysis, closing out a trial, reporting and interpreting results, and issues in multicenter trials.

STA 5323. Introduction to Mathematical Statistics (3). Prerequisite: MAC 2313 or equivalent. This course discusses topics such as distributions of random variables, conditional probability and independence, multivariate distributions, sampling distributions, Bayes' rule, counting problems, expectations.

STA 5325. Mathematical Statistics (3). Prerequisites: STA 4442 or STA 5440 and either MAC 2313 or STA 5326. This course explores topics such as sufficiency, point estimation, confidence intervals, hypothesis testing, regression, linear models, Bayesian models.

STA 5326. Distribution Theory and Inference (3). Prerequisites: MAC 2313; at least one previous course in statistics or probability. This course is an introduction to probability, random variables, distributions, limit laws, conditional distributions, and expectations.

STA 5327. Statistical Inference (3). Prerequisites: STA 5166 and STA 5326. This course introduces students to the basics of statistical inference and its applications. The overarching goal is to introduce statistical techniques to estimate and provide uncertainty measures of the estimates themselves of key quantities of a population e.g. mean, median, location shift, variance, etc. using the observed sample.

STA 5334. Limit Theory of Statistics (3). Prerequisite: STA 5327. This course focuses on topics such as convergence of distribution and random variables, laws of large numbers, central limit theorems, asymptotic distributions, asymptotic efficiency, rates of convergence, the weak invariance principle.

STA 5363. Fundamental Algorithms for Statistical Data (3). Prerequisites: MAC 2313, MAS 3105, STA 2122, or instructor permission. Familiarity with the python programming language is encouraged. This course provides an introduction to the fundamental elements necessary for conducting research in Machine Learning, Data Science, and Computer Vision. Students learn fundamental data structures, algorithms and numerical methods for successful research and develop the skills to confidently write efficient and manageable experimental/research code in Python.

STA 5440. Introductory Probability I (3). Prerequisite: MAC 2311. This course discusses topics such as random variables, probability of random variables, generating functions, central limit theorem, laws of large numbers.

STA 5447. Probability Theory (3). Prerequisites: STA 5326 and STA 5446.

STA 5507. Applied Nonparametric Statistics (3). Prerequisite: A course in statistics above STA 1013 or instructor permission. This course focuses on applications of nonparametric tests, estimates, confidence intervals, multiple comparison procedures, multivariate nonparametric methods, and nonparametric methods for censored data.

STA 5635. Applied Machine Learning (3). Prerequisite: STA 3032 or instructor permission. This course is a hands-on introduction to statistical methods for supervised, unsupervised, and semi-supervised learning. It explores fundamental techniques including but not limited to Support Vector Machines, Decision Trees, Linear Discriminant Analysis, Random Forests, Neural Networks, and different flavors of Boosting.

STA 5666. Statistics for Quality and Productivity (3). Prerequisites: STA 5167 or instructor permission, and either STA 4322 or STA 5126. This course discusses statistics for quality control and productivity; graphical methods; control charts; design and experiment for product and process improvement.

STA 5707. Applied Multivariate Analysis (3). Prerequisite: One of STA 5167, STA 5207, or STA 5327. This course discusses inference about mean vectors and covariance matrices, canonical correlation, principal components, discriminant analysis, cluster analysis, and computer techniques.

STA 5721. High-Dimensional Statistics (3). Prerequisites: STA 5167 and STA 5326. Recommended prerequisite: STA 5168. This course covers a range of modern statistical topics in high dimensional modeling and analysis. The course teaches methods, theory and computation with rich high-dimensional data applications from signal processing, machine learning, bioinformatics and econometrics.

STA 5807r. Topics in Stochastic Processes (3). Prerequisite: STA 5326. May be repeated to a maximum of twelve semester hours.

STA 5856. Time Series and Forecasting Methods (3). Prerequisite: STA 5126, QMB 3200, or equivalent. This course explores autoregressive, moving average and mixed models, autocovariance and autocorrelation functions, model identification, forecasting techniques, seasonal model identification estimation and forecasting, intervention and transfer function model identification, estimation and forecasting.

STA 5906r. Directed Individual Study (1–12). (S/U grade only). May be repeated to a maximum of twelve semester hours.

STA 5910r. Supervised Research (0–5). (S/U grade only). May be repeated to a maximum of five semester hours. A maximum of three semester hours may apply to the master's degree.

STA 5920r. Statistics Colloquium (1). (S/U grade only). May be repeated to a maximum of twelve semester hours.

STA 5934r. Selected Topics in Statistics, Probability, or Operations Research (2–3). May be repeated to a maximum of twelve semester hours.

STA 5939. Introduction to Statistical Consulting (3). Prerequisite: STA 5167, or STA 5327, or instructor permission. This course consists of the formulation of statistical problems from client information, the analysis of complex data sets by computer, and practical consulting experience.

STA 5940r. Supervised Consulting (1–3). (S/U grade only). May be repeated to a maximum of twelve semester hours.

STA 5941r. Supervised Teaching (1–5). (S/U grade only). May be repeated to a maximum of five semester hours. A maximum of three semester hours may apply to the master's degree.

STA 5945r. Internship in Statistics (0-6). In this course, students gain practical experience in the application of statistical methods working as an intern at an appropriate company or government agency performing statistical analysis under supervision of a corporate, or government. This course may also be taken by students working on an approved data-based grant project in another department on campus or on an interdisciplinary grant project involving statistics and another department on campus. In this case, the affiliate faculty member will be the student's supervisor on the project.

STA 5971Cr. Thesis (3–6). (S/U grade only). Six semester hours required.

STA 6174r. Advanced Methods in Epidemiology (3). Prerequisites: STA 5167 and STA 5325. This course presents advanced methods for describing, analyzing, and modeling data from observational studies. The initial offering includes introductions to meta-analytic methods, bootstrap methods, and randomization tests. Topics vary with each offering. May be repeated up to a maximum of six semester hours.

STA 6246r. Advanced Probability in Applied Statistics (2–3). Prerequisite: STA 5167. May be repeated to a maximum of twelve semester hours.

STA 6341. Modern Robust Statistics (3). Prerequisites: STA 5167 or STA 5207 or SYA 5208 or instructor permission. This course covers a wide range of methods, computational tools, and theoretical topics related to modern robust statistics, as well as real world applications of statistics, biostatistics, machine learning, finance, signal processing, and related research areas.

STA 6346. Advanced Probability and Inference I (3). Prerequisites: STA 5326 and STA 5327. This course covers the basics of the probability theory, random elements, and stochastic processes; characteristic functions and probability inequalities; central limit theorems; elements of Markov dependence and martingale theory; common scholastic processes arising in biostatistics; advanced treatment of sufficient statistics, exponential families, estimation, and testing; as well as elements of asymptotic theory of statistical inference.

STA 6448. Advanced Probability and Inference II (3). Prerequisites: STA 5326 and STA 5327. This course covers unbiased and locally most powerful tests (including the multiparameter case); envelope power function; best average power test; Bayes and empirical Bayes procedures; likelihood, quasi likelihood, and profile likelihood; order statistics and empirical distributions; general central limit theorems; variance stabilizing transformations; U-statistics; least squares, weighted least squares, and generalized least squares estimation; generalized estimating equations; asymptotic theory for BAN estimators; asymptotic theory for likelihood ratio, Wald, and score tests; log-linear models; asymptotics for linear inference; as well as robust statistical inference.

STA 6468r. Advanced Topics in Probability and Statistics (2–3). May be repeated to a maximum of twelve semester hours.

STA 6557. Object Data Analysis (3). Prerequisite: One of STA 5707, STA 5327, or STA 5746. This course covers the most inclusive type of data analysis known in statistics; examples of such data in astronomy, biology, digital imagery, medical imaging, computer vision, pattern recognition, astrophysics, learning, Earth sciences including meteorology and geology; introduction to abstract manifolds, tangent bundles, embedding, Riemannian structures; sample spaces with a manifold structure; foundations of nonparametric statistics on manifolds: location and spread parameters for distributions on manifolds; large sample theory on manifolds, density, and function estimation on manifolds; nonparametric inference on manifolds; statistical analysis on special manifolds arising in statistics: directional and axial data analysis, projective, affine, and similarity shape data analyses, size-and-shape data analysis, diffusion tensor image analysis; concrete case studies in astronomy, image analysis, medical imaging: MRI, CT, Confocal Laser Tomography, eye imaging, brain imaging, bioinformatics, computer vision, and 3D scene recognition.

STA 6709. Spatial Statistics (3). Prerequisites: STA 5167 and STA 5327; or instructor permission. This course examines methods for the analysis of spatial data, including geostatistical data, lattice data, and point patterns. The course also includes theory and applications of basic principles and techniques.

STA 6723. Statistical Optimization (3). Prerequisite: STA 5167. The course teaches matrix algebra and optimization which are at the core of modern statistics. The course also involves a wide range of real world applications in statistics, biostatistics, machine learning, finance, signal processing, and related research areas.

STA 6906r. Directed Individual Study (1–12). (S/U grade only). May be repeated.

STA 6980r. Dissertation (1–12). (S/U grade only).

STA 8964. Preliminary Doctoral Examination (0). (P/F grade only.)

STA 8976. Master's Thesis Defense (0). (P/F grade only.)

STA 8985. Defense of Dissertation (0). (P/F grade only.)

2024-2025 GRADUATE BULLETIN