Undergraduate Department of
College of Arts and Sciences
Chair: Xufeng Niu; Associate Chair: Fred Huffer; Director, Statistical Consulting Center: Ramsier; Professors: Barbu, Chicken, Huffer, Niu, Patrangenaru, She, Sinha, Slate, Srivastava, W. Wu; Associate Professors: Cao, Mai, H. Zhang, J. Zhang; Assistant Professors: Barrientos, Bradley, Huang, Lin, C. Wu; Teaching Professor: Ramsier; Senior Lecturer: Bose; Teaching Faculty II: Shows; Professors Emeriti: Hollander, Lin, McGee, Meeter, Sethuraman, Zahn
The Department of Statistics offers a program leading to the Bachelor of Science (BS) degree in statistics. Statistics is the science of analyzing random events and their associated data. The goals of the analysis are to describe the properties and characteristics of the data visually and numerically, to provide a model for the underlying events which takes into account the randomness of the phenomena, and to make accurate predictions of future events. In the study of statistics, students use and enrich their mathematical expertise and orient their study of the statistical methodology toward useful and relevant purposes in society. Significant opportunities for well-trained persons in statistics arise in many career environments, such as the social sciences, the natural sciences, business, industry, the health services, and government services. Flexible, individually-planned programs of study for minors or majors, including an honors option, are available. Interested students should contact the director of the undergraduate program for more information.
The Department of Statistics offers a wide selection of undergraduate courses in statistical methods for nonmajors with minimal background in mathematics. One of STA 2023, 2122, 2171, or 3032 is a prerequisite for the remaining courses in the series, which are STA 3024, 4202, 4203, 4222, 4502, 4634, 4664, 4702, and 4853.
The department offers a combined bachelor's/master's pathway designed for academically gifted students who wish to pursue an accelerated program culminating in a BS degree in Statistics and an MS degree in Statistics, Statistical Data Science, or Biostatistics. This pathway allows up to twelve semester hours of coursework to be dually counted toward both the BS and MS degrees.
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.
Computer Skills Competency
All undergraduates at Florida State University must demonstrate basic computer skills competency prior to graduation. As necessary computer competency skills vary from discipline to discipline, each major determines the courses needed to satisfy this requirement. Undergraduate majors in statistics satisfy this requirement by earning a grade of "C–" or higher in STA 3024.
State of Florida Common Program Prerequisites
The state of Florida has identified common program prerequisites for this University degree program. Specific prerequisites are required for admission into the upper-division program and must be completed by the student at either a community college or a state university prior to being admitted to this program. Students may be admitted into the University without completing the prerequisites, but may not be admitted into the program.
At the time this document was published, some common program prerequisites were being reviewed by the state of Florida and may have been revised. Please visit https://dlss.flvc.org/admin-tools/common-prerequisites-manuals for a current list of state-approved prerequisites.
The following lists the common program prerequisites or their substitutions, necessary for admission into this upper-division degree program:
- COP XXXX: one scientific programming course for three credit hours designed for computer science majors
- MAC X311
- MAC X312
- MAC X313
- BSC XXXX/XXXXL or CHM XXXX/XXXXL or PHY XXXX/XXXXL or GLY XXXX/XXXXL: two laboratory-based science courses for four to eight credit hours designed for science majors
- STA 2XXX
Note: A "C" grade or better in all coursework is required for admission.
Requirements for a Major in Statistics
Please review all college-wide degree requirements summarized in the "College of Arts and Sciences" chapter of this General Bulletin.
The major requires thirty-three total semester hours. Twenty-one of those hours are required statistics courses, including STA 3024 and either STA 4321 or 4442. The additional fifteen semester hours are elective and may be selected from any other 3000- or 4000-level courses with the STA prefix.
Additional requirements include MAC 2311, MAC 2312, and MAS 3105. A grade of "C–" or better must be earned in each statistics or mathematics course counted toward the major. At least seventeen semester hours of courses counted toward the major must be taken at Florida State University. Statistics courses taken at other universities or colleges must be approved by the department.
Students interested in pursuing a course of study in applied statistics are encouraged to take STA 3032, 3064, 4202, and 4203. This provides a strong background in practical data analysis which will be attractive to future employers, as well as completing most of the requirements for a SAS certificate in Programming and Data Analysis.
Students anticipating graduate study in statistics are strongly encouraged to take the STA 4321 and 4322 sequence and additional mathematics courses such as MGF 3301, MAA 4226, MAA 4227, and MTG 4302.
Double Major Overlap Policy
For students double majoring in statistics and another discipline, the department's overlap policy permits six credit hours of coursework counted toward the other major to be also counted toward the statistics major requirements. This overlap limit excludes prerequisite coursework and collateral mathematics courses (MAC 2311, MAC 2312, and MAS 3105).
The minor may be in any of the departmental or interdepartmental fields approved by the College of Arts and Sciences. A minor in mathematics may include MAC 2311, 2312, and MAS 3105.
Honors in the Major
The Department of Statistics offers honors in the major to encourage talented students to undertake independent research. For requirements and other information, see the "University Honors Office and Honor Societies" chapter of this General Bulletin.
Requirements for a Minor in Statistics
Required are twelve semester hours in statistics courses, including one of STA 2122, 2171, 3024, 3032, 4442, or 4321 with the remaining three coming from any STA course numbered at the 3000-level or higher. Courses should be selected in consultation with the director of the undergraduate statistics program. A grade of "C–" or better must be earned in each course counted toward the minor. At least six semester hours in statistics courses counted toward the minor must be taken in the Department of Statistics at Florida State University. Statistics courses taken at other universities or colleges must be approved by the department. Contact the department for a full list of requirements and courses applicable to the minor.
Examples of Minor Options
- A minor in statistical methodology with minimal mathematical prerequisites: STA 2122 or 2171, plus nine semester hours selected from any of STA 3024, 3064, 4173, 4202, 4203, 4222, 4502, and 4664.
- A minor with statistical theory as well as methodology: STA 4321 and 4322, plus six hours selected from any of STA 4102, 4202, 4203, 4222, 4502, 4702, and 4853.
Combined Bachelor's/Master's Degree Pathway in Statistics
The combined bachelor's/master's pathway in the Department of Statistics is designed for academically strong students who wish to pursue an accelerated program culminating in a Bachelor of Science (BS) degree in statistics and a Master of Science (MS) degree in Statistics, Statistical Data Science, or Biostatistics. Upon approval, this program allows up to 12 graduate hours to be shared with, or double-counted toward, both a BS and an MS degree.
An undergraduate student wishing to enroll in this pathway must meet the following criteria:
- Completion of at least twelve semester hours of mathematics or statistics in the undergraduate statistics major at Florida State University with a GPA of at least 3.2 in those courses.
- Completion of at least sixty semester hours at Florida State University with a GPA of at least 3.0. Transfer students must have completed at least two semesters and twenty-four semester hours at FSU with the same minimum GPA.
Undergraduate students may apply as early as the second semester of their sophomore year. If accepted, they should take the GRE at the end of their junior year and apply to the graduate school during the first semester of their senior year.
For more information, please visit https://stat.fsu.edu/undergraduate-program/combined-bachelorsmasters-pathway.
Undergraduate Certificate in SAS Programming and Data Analysis
The FSU Department of Statistics offers a certificate in Statistical Analysis System (SAS) Programming and Data Analysis. The certificate is designed to provide students with in-demand programming and statistical computing skills using one of the leading statistical software packages. Focus is placed on applications that require data management and statistical analyses. A certificate with honors option is available.
The undergraduate certificate requires twelve semester hours consisting of one required core course, STA 3024, and three elective courses with a SAS component selected from the following list: STA 3064, 4173, 4202, 4203, 4664, 4702, 4853, 4930 (depending on the topic—check with an advisor) and 4931. Students seeking the honors designation may take STA 5066 in place of STA 3024 as the required core course or augment STA 3024 for honors credit. The coursework will also meet the requirements for students seeking a minor in statistics and can be embedded into a program for those students seeking a major in statistics.
Applicants must also submit a portfolio binder of their SAS coursework. The binder will include major assignments or projects from the courses taken in the certificate program with all four courses being represented. The completed portfolio will demonstrate several areas of SAS skills that are deemed valuable for public sector, private sector, or graduate school work. Students interested in the certificate must apply before completion of their second course in the program. The certificate application and more program details may be found at https://stat.fsu.edu/sas-certificate.
Definition of Prefixes
QMB—Quantitative Methods in Business
SCE 4939r. Seminar in Contemporary Science, Mathematics, and Science Education (1).
Note: For the description of the course above, see "Science Education" in the School of Teacher Education chapter of this General Bulletin.
EGN 3443. Statistical Topics in Engineering (3). Prerequisite: MAC 2312. This course explores basic statistical analysis, samples and populations, variability, hypothesis formulation, and data analysis. Use of computer software and interpretation of results.
IDS 2400. Understanding Uncertainty: Games of Skill and Chance (3). This course introduces and employs two mathematical tools useful in quantifying uncertainty: probability and statistics. Questions are considered in the context of games of chance, such as card and casino games, and games of skill, such as sporting events.
QMB 3200. Quantitative Methods for Business Decisions (3). This course examines classical and modern decision-making techniques based on probabilistic concepts. Emphasizes applications to all areas of business.
STA 1013. Statistics Through Example (3). This course provides students with a background in applied statistical reasoning. Fundamental topics are covered including graphical and numerical description of data, understanding randomness, central tendency, correlation versus causation, line of best fit, estimation of proportions, and statistical testing. Statistical thinking, relevant ideas, themes, and concepts are emphasized over mathematical calculation. In this class students learn many of the elementary principles that underlie collecting data, organizing it, summarizing it, and drawing conclusions from it.
STA 1220. In My Opinion: Introduction to Designing, Conducting and Analyzing Surveys (3). This course teaches the methods and concepts behind creating and conducting surveys and the statistical tools needed to analyze data gathered from them. Students participate in data collection from different sources for individual- and class-designed surveys.
STA 2023. Fundamental Business Statistics (3). Miscellaneous requirement: Two years of high school algebra is recommended. Special Note: High school students who earn a "3" or better on the AP Statistics Exam may elect to be given credit for STA 2023. This course covers statistical applications in business, involving graphical and numerical descriptions of data, data collection, correlation and simple linear regression, elementary probability, random variables, binomial and normal distributions, sampling distributions, and confidence intervals and hypothesis tests for a single sample. This course prepares students for further study and job preparation in the field of Business. The course emphasizes understanding of data and interpretation of statistical analyses, and requires students to think of data, and report the results of their analyses, in context.
STA 2122. Introduction to Applied Statistics (3). Prerequisite: MAC 1105. Special note: No credit given for STA 2122 if a grade of "C-" or better is earned in STA 2171, STA 3032, or QMB 3200. This course covers normal distributions, sampling variation, confidence intervals, hypothesis testing, one-way and two-way analysis of variance, correlation, simple and multiple regression, contingency tables and chi-square tests, and non-parametric statistics. No credit given for STA 2122 if a grade of "C–" or better is earned in STA 2171, STA 3032, or QMB 3200.
STA 2171. Statistics for Biology (4). Prerequisite: MAC 2311 and biology major status or departmental approval. This course provides an introduction to statistics emphasizing applications to biology. Topics include: descriptive statistics, elementary probability, the binomial and normal distributions, confidence intervals and hypothesis tests for means and proportions, correlation and regression, contingency tables and goodness-of-fit tests as well as analysis of variance.
STA 3024. SAS for Data and Statistical Analyses (3). Prerequisite: STA 2023 or STA 2122. This course introduces students to the SAS programming language in a lab-based format. The objective is for students to develop programming and statistical computing skills to address data management and analysis issues using SAS. The course also provides a survey of some of the most common data analysis tools in use today and provides decision-making strategies in selecting the appropriate methods for extracting information from data.
STA 3032. Applied Statistics for Engineers and Scientists (3–5). Prerequisite: MAC 2312. This course covers calculus-based probability, discrete and continuous random variables, joint distributions, sampling distributions, and the central limit theorem. Topics include descriptive statistics, interval estimates and hypothesis tests, ANOVA, correlation, simple and multiple regression, analysis of categorical data, and statistical quality control.
STA 3064. Introduction to Statistical Modeling with SAS (3). Prerequisites: STA 2122 and STA 3024. This course covers the following topics utilizing the SAS software: ANOVA, linear modeling, logistic regression, bootstrap sampling, simulation using the data step, and some additional analytic topics.
STA 4102. Computational Methods in Statistics I (3). Prerequisites: At least one statistics above STA 1013, some programming experience, or instructor permission. This course utilizes Matlab and a programming language (C/Fortran) is used. The course focuses on topics such as floating point arithmetic, numerical matrix analysis, multiple regression analysis, non-linear optimization, root finding, numerical integration, Monte Carlo sampling, survey of density estimation.
STA 4103. Computational Methods in Statistics II (3). Prerequisite: STA 4102 or instructor permission. This course utilizes Matlab and a programming language (C/Fortran) is used. The course is a continuation of STA 4102 in computational techniques for linear and non-linear statistics. The course also explores topics such as statistical image understanding, elements of pattern theory, simulated annealing, Metropolis-Hastings algorithm, Gibbs sampling.
STA 4173. Fundamentals of Biostatistics (3). Prerequisite: A previous upper division course in statistics or instructor permission. This course introduces students to the statistical methods used to design and analyze studies of the occurrence of disease in human populations.
STA 4202. Analysis of Variance and Design of Experiments (3). Prerequisite: STA 2122, STA 2171, STA 3032, or QMB 3200. This course focuses on topics such as one and two-way classifications, nesting, blocking, multiple comparisons, incomplete designs, variance components, factorial designs, and confounding.
STA 4203. Applied Regression Methods (3). Prerequisite: STA 2122, STA 2171, STA 3032, STA 4322, or QMB 3200. This course focuses on topics such as general linear hypothesis, multiple correlation and regression, residual analysis, and model identification.
STA 4222. Sample Surveys (3). Prerequisite: A statistics course above STA 1013 or instructor permission. This course focuses on simple, stratified, systematic, and cluster random sampling as well as ratio and regression estimation and multistage sampling.
STA 4321. Introduction to Mathematical Statistics (3). Prerequisite: MAC 2313. This course focuses on topics such as distribution of random variables, conditional probability and independence, multivariate distributions, sampling distributions, Bayes' rule, counting problems, expectations. Credit not given for both STA 4321 and STA 4442.
STA 4322. Mathematical Statistics (3). Prerequisites: STA 4321 and MAC 2313. This course focuses on topics such as sufficiency, point estimation, confidence intervals, hypothesis testing, regression, linear models, and Bayesian analysis. Subsequent credit for STA 5325 is not permitted.
STA 4442. Introductory Probability I (3). Prerequisite: MAC 2312. This course covers various topics including, but not exclusively: random variables, probability distributions, independence, sums of random variables, generating functions, central limit theorem, and the laws of large numbers. Credit is not given for both STA 4321 and STA 4442, and subsequent credit for STA 5440 is not permitted.
STA 4502. Applied Nonparametric Statistics (3). Prerequisite: A statistics course above STA 1013 or instructor permission. This course explores topics such as the application of nonparametric tests, estimates, confidence intervals, and multiple comparison procedures.
STA 4634. 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 4664. Statistics for Quality and Productivity (3). Prerequisites: STA 4322 or instructor permission, as well as STA 2122 or STA 2171 or STA 3032 or STA 4442. This course explores topics such as Deming's ideas, graphical methods, control charts, and design of experiments for product and process improvement.
STA 4702. Applied Multivariate Analysis (3). Prerequisite: STA 4203 or STA 4322. This course focuses on many topics including principal components and factor analysis, canonical correlation, discriminant analysis, multivariate analysis of variance, multidimensional contingency tables, cluster analysis. Subsequent credit for STA 5707 is not permitted.
STA 4853. Time Series and Forecasting Methods (3). Prerequisites: QMB 3200 or equivalent, STA 2122, STA 2171, STA 3032, and knowledge of PCs or UNIX. This course focuses on many topics including 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. Subsequent credit for STA 5856 is not permitted.
STA 4905r. Directed Individual Study (2–3). (S/U grade only.) May be repeated to a maximum of twelve semester hours.
STA 4930r. Selected Topics in Statistics, Probability, or Operations Research (2–3). May be repeated to a maximum of twelve semester hours.
STA 4970r. Honors Thesis in Statistics (3). Students selected by the University and the department honors programs may take this course. Consent of the thesis advisor is mandatory. May be repeated to a maximum of six semester hours.
STA 5066. Data Management and Analysis with SAS (3).
STA 5067. Advanced Data Management and Analysis with SASS (3).
STA 5106. Computational Methods in Statistics I (3).
STA 5107. Computational Methods in Statistics II (3).
STA 5126. Introduction to Applied Statistics (3).
STA 5166. Statistics in Applications I (3).
STA 5167. Statistics in Applications II (3).
STA 5168. Statistics in Applications III (3).
STA 5172. Fundamentals of Biostatistics (3).
STA 5176. Statistical Modeling with Application to Biology (3).
STA 5179. Applied Survival Analysis (3).
STA 5198. Epidemiology for Statisticians (3).
STA 5206. Analysis of Variance and Design of Experiments (3).
STA 5207. Applied Regression Methods (3).
STA 5208. Linear Statistical Models (3).
STA 5225. Sample Surveys (3).
STA 5238. Applied Logistic Regression (3).
STA 5244. Clinical Trials (3).
STA 5323. Introduction to Mathematical Statistics (3).
STA 5325. Mathematical Statistics (3).
STA 5326. Distribution Theory and Inference (3).
STA 5327. Statistical Inference (3).
STA 5334. Limit Theory of Statistics (3).
STA 5440. Introductory Probability I (3).
STA 5446. Probability and Measure (3).
STA 5447. Probability Theory (3).
STA 5507. Applied Nonparametric Statistics (3).
STA 5635. Applied Machine Learning (3).
STA 5666. Statistics for Quality and Productivity (3).
STA 5707. Applied Multivariate Analysis (3).
STA 5721. High-Dimensional Statistics (3).
STA 5807r. Topics in Stochastic Processes (3).
STA 5856. Time Series and Forecasting Methods (3).
STA 5906r. Directed Individual Study (1–12). (S/U grade only.)
STA 5910r. Supervised Research (0–5). (S/U grade only.)
STA 5920r. Statistics Colloquium (1). (S/U grade only.)
STA 5934r. Selected Topics in Statistics, Probability, or Operations Research (2–3).
STA 5939. Introduction to Statistical Consulting (3).
STA 5940r. Supervised Consulting (1–3). (S/U grade only.)
STA 5941r. Supervised Teaching (1–5). (S/U grade only.)
STA 5945r. Internship in Statistics (0-6).
STA 6174r. Advanced Methods in Epidemiology (3).
STA 6246r. Advanced Topics in Applied Statistics (2–3).
STA 6346. Advanced Probability and Inference I (3).
STA 6448. Advanced Probability and Inference II (3).
STA 6468r. Advanced Topics in Probability and Statistics (2–3).
STA 6557. Object Data Analysis (3).
STA 6709. Spatial Statistics (3).
STA 6906r. Directed Individual Study (1–12). (S/U grade only.)
For listings relating to graduate coursework for thesis, dissertation, and master's and doctoral examinations and defense, consult the Graduate Bulletin.