Undergraduate Department of Statistics

College of Arts and Sciences

Chair: Eric Chicken; Director, Undergraduate Studies and Statistical Consulting Center: Ramsier; Director, Graduate Studies: Barbu; Professors: Barbu, Cao, Chicken, Huffer, Mai, Niu, Patrangenaru, She, Sinha, Slate, Srivastava, Wu, H. Zhang, J. Zhang; Associate Professors: Bradley; Assistant Professors: Barrientos, Bhattacharya, Huang, Jauch, Lin, Liu, Loyal, Stewart, Tong, Xie; Teaching Professor: Ramsier; Senior Lecturer: Bose; Teaching Faculty III: Shows; Research Faculty I: Y. Zhang; Professors Emeriti: Hollander, Lin, McGee, Meeter, Sethuraman

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 considers 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, 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 access to for computation for use with coursework and research. The department holds licenses to several popular statistical software packages and has connections to other statistical computing tools. This enables students to work on computationally intensive projects.

Digital Literacy Requirement

Students must complete at least one course designated as meeting the Digital Literacy Requirement with a grade of “C–” or higher. Courses fulfilling the Digital Literacy Requirement must accomplish at least three of the following outcomes:

Evaluate and interpret the accuracy, credibility, and relevance of digital information
Evaluate and interpret digital data and their implications
Discuss the ways in which society and/or culture interact with digital technology
Discuss digital technology trends and their professional implications
Demonstrate the ability to use digital technology effectively
Demonstrate the knowledge to use digital technology safely and ethically

Each academic major has determined the courses that fulfill the Digital Literacy requirement for that major. Students should contact their major department(s) to determine which courses will fulfill their Digital Literacy 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 for Statistics

The Florida Virtual Campus (FLVC) houses the statewide, internet-based catalog of distance learning courses, degree programs, and resources offered by Florida's public colleges and universities, and they have developed operational procedures and technical guidelines for the catalog that all institutions must follow. The statute governing this policy can be reviewed by visiting https://www.flsenate.gov/Laws/Statutes/2021/1006.73.

FLVC has identified common program prerequisites for the degree program in Statistics. To obtain the most up-to-date, state-approved prerequisites for this degree, visit: https://cpm.flvc.org/programs/61/200.

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.

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.

Major

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 the mathematics courses MAC 2311, MAC 2312, and MAS 3105. A grade of “C–” or better must be earned in each statistics and 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.

Options

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).

Minor

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 first semester of their junior year. If accepted, they should take the GRE by the beginning of their senior 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 typically used in the field of data sciences. A certificate with an 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, 4853, 4930 (depending on the special 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 of their SAS coursework. The portfolio 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

EGN—Engineering: General

IDS—Interdisciplinary Studies

QMB—Quantitative Methods in Business

STA—Statistics

Undergraduate Courses

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.

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.

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. The course prepares students for further study and job preparation in Medicine, Dentistry, other healthcare professions, Veterinary Medicine, Zoology, and Botany. Emphasis is placed on understanding of data and interpretation of statistical analyses in context.

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 3732. Statistical Tools for Data Analytics (3). Prerequisites: STA 3024 or instructor permission. This course provides statistical perspectives on the methods and software tools used in the data analytics discipline. Students gain practical experience with the applications used to prepare, explore, visualize, experiment with, and make predictions from data. The role of the data analyst in the data science workflow is addressed by completing assignments involving actual data.

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 in the Major Research (1–6). In this course, students accepted into the Honors in the Major program complete an original research or creative project in their major area of study. This course must be repeated at least twice to complete a minimum of six (6) credit hours total, but may be repeated up to a maximum of twelve credit hours in total.

For listings relating to graduate coursework, consult the Graduate Bulletin.

2024-2025 UNDERGRADUATE BULLETIN