## Department of Statistics

### College of Arts and Sciences

**Web Page:** http://stat.fsu.edu/

**Chair:** Xu-Feng Niu; **Associate Chair:** McGee; **Director, Statistical Consulting Center:** Ramsier; **Director, Graduate Students:** Barbu; **Professors:** Chicken, Huffer, McGee, Niu, Patrangenaru, Sinha, Slate, Srivastava; **Associate Professors:** Barbu, She, Wu, Zhang; **Assistant Professors:** Bradley, Linero, Mai, Tao, Yang, Zhang; **Teaching Professor:** Ramsier; **Senior Lecturer: **Bose; **Professors Emeriti:** Hollander, Lin, Meeter, Sethuraman, Zahn

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 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 http://sas.stat.fsu.edu/ for details).

## Facilities

The Department of Statistics provides statistical consultation on University 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 PC’s 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, the National Imagery and Mapping Agency, and the United States Army Research Office.

## 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 and a sequence of real analysis courses are desirable, but not required. A score at the 65th percentile or higher in quantitative reasoning and at least the 35th percentile in verbal reasoning on the Graduate Record Examinations (GRE) is 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 statistical data science, which results in an MS in statistics with major in statistical data science;

or

- A four-semester program emphasizing mathematical statistics, which results in an MS in statistics;

or

- A four-semester program emphasizing applied statistics, which results in an MS in statistics;

or

- A four-semester program emphasizing biostatistics, which results in an MS in biostatistics degree;

or

- Undergraduates may enroll in a five-year combined BS/MS degree. The graduate degree earned is the master’s degree emphasizing applied statistics.

The MS in statistics with 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 degrees 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 Web site at http://stat.fsu.edu.

## 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 http://stat.fsu.edu.

## Definition of Prefix

**STA**—Statistics

## Graduate Courses

**STA** **5066.** **Data Management and Analysis with SAS (3)**. Prerequisite: Some exposure to introductory statistics 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 the student 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, an overview of SAS statistical procedures including statistical graphics, an introduction to SAS’s macro capabilities for automating repeated analyses, and an introduction to IML Plus, SAS’s recently released interface to its interactive matrix language.

**STA** **5067.** **Advanced Data Management and Analysis with SAS (3)**. Prerequisite: STA 5066 or instructor permission. 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, 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** **5176.** **Statistical Modeling with Application to Biology (3)**. Prerequisite: STA 4442 or STA 5440. This course covers maximum likelihood principle, missing data and EM algorithm; assessment tools such as bootstrap and cross-validation; Markov chain and hidden Markov models; classification and regression trees (CART); Bayesian models and Markov Chain Monte Carlo algorithms.

**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** **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.

**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 2171. This course is an applied introduction to logistic regression, one of the most commonly used analytic tools in biomedical 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** **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** **5446.** **Probability and Measure (3)**. Prerequisites: MAA 4227, MAA 5307, or the equivalent. This course explores classes of sets, probability measures, construction of probability measures, random variables, expectation and integration, independence and product measures.

**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** **5676.** **Reliability Theory and Life Testing (4)**. Prerequisite: A basic course in probability and statistics.

**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** **5746.** **Multivariate Analysis (3)**. Prerequisite: STA 5327.

**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** **5938.** **Topics in Medical Consulting (3)**. Prerequisite: STA 2171. This is a “hands-on” course in consulting. In this course, two to four reasonably complex problems are identified each time the course is offered, and the investigators present the problem to the class. Statistical topics covered in class are those identified by the class as required to solve the problems presented.

**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** **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** **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** **6466.** **Advanced Probability (3)**. Prerequisite: STA 5447.

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

**STA** **6555.** **Nonparametric Curve Estimation (3)**. Prerequisite: STA 5327 or instructor permission. This course explores estimation of regression and density functions and their derivatives where no parametric model is assumed. Kernel, local polynomial, spline and wavelet methods are used. Emphasis is on analysis and applications of the smoothing techniques and data-based smoothing parameter selectors.

**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** **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** **8966.** **Master’s Comprehensive 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.)

SURVEYING AND RELATED AREAS:

see Civil and Environmental Engineering

TAX ACCOUNTING:

see Accounting

TEACHING ENGLISH AS A SECOND LANGUAGE:

see Teacher Education