Statistics (STA)

STA 1301  Statistical Reasoning: A Guide to the Unknown  (3)  
Pre-requisite(s): Freshman standing and consent of statistics undergraduate faculty advisor  
Philosophical, ethical, and sociological issues related to statistical uncertainty and randomness.
STA 1380  Elementary Statistics  (3)  
Introduction to traditional statistical concepts including descriptive statistics, binomial and normal probability models, tests of hypotheses, linear correlation and regression, two-way contingency tables, and one-way analysis of variance. Credit may not be obtained after receiving credit in STA 2381 or 3381.
STA 1V9R  Research  (3)  
Pre-requisite(s): Consent of the instructor  
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.
STA 2300  Introduction to Data Science  (3)  
Cross-listed as CSI 2300  
Principles of data science, including problem workflow, variable types, visualization, modeling, programming, data management and cleaning, reproducibility, and big data.
STA 2381  Introductory Statistical Methods  (3)  
Pre-requisite(s): A grade of C or above in MTH 1321  
Parametric statistical methods. Topics range from descriptive statistics through regression and one-way analysis of variance. Applications are typically from biology and medicine. Computer data analysis is required.
STA 2450  Introduction to Computing for the Mathematical and Statistical Sciences  (4)  
Computer programming for mathematical scientists with emphasis on designing algorithms, problem solving, and coding practices. Topics include development of programs from specifications; appropriate use of data types; functions; modular program organization; documentation and style; and version control and collaborative programming.
STA 2V9R  Research  (3)  
Pre-requisite(s): Consent of the instructor  
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.
STA 3300  History of Statistics: English Origins  (3)  
Pre-requisite(s): STA 1380 or STA 2381 or STA 3381 or QBA 2302  
Examines historical beginnings of the statistics discipline in England. Considers philosophical tenets from the Bayesian and frequentist perspectives as well as the debates between Fisher and Pearson. Emphasizes significance of such sites as Rothamsted Research Station, Bletchley Park, and the University of Cambridge, all linked to the birthplace of modern statistics.
STA 3310  Sports Analytics I  (3)  
Pre-requisite(s): STA 3381  
Combines classical statistical methods with cutting-edge data science tools to communicate findings and wield influence over decisions within sports organizations. Fosters critical thinking, equipping students with statistical techniques for data analytics, and mastering data visualization to facilitate data-driven choices in sports.
STA 3375  Technologies for Sports Analytics  (3)  
Pre-requisite(s): STA 2300 and STA 2450  
Concepts in big data analytics primarily applied to topics in sports focusing on graphical methods through dashboards and inferential methods.
STA 3381  Probability and Statistics  (3)  
Pre-requisite(s): A grade of C or above in MTH 1322  
Introduction to the fundamentals of probability, random variables, discrete and continuous probability distributions, expectations, sampling distributions, topics of statistical inference such as confidence intervals, tests of hypotheses, and regression.
STA 3386  Regression Analysis  (3)  
Pre-requisite(s): MTH 2311, MTH 2321, and STA 3381 A development of regression techniques including simple linear regression, multiple regression, logistic regression and Poisson regression with emphasis on model assumptions, parameter estimation, variable selection and diagnostics  
STA 3V90  Undergraduate Research in Statistics  (1-3)  
Pre-requisite(s): Consent of instructor  
Independent study or research in topics not available in other courses. Maximum of four hours will count toward the degree.
STA 3V9R  Research  (3)  
Pre-requisite(s): Consent of the instructor  
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.
STA 4330  SAS Programming for Statistical Science  (3)  
Pre-requisite(s): STA 2381 or 3381  
Concepts in SAS programming including methods to establish and transform SAS data sets, perform statistical analyses, and create general customized reports. Methods from both BASE SAS and SAS SQL will be considered.
STA 4350  Statistical Machine Learning  (3)  
Pre-requisite(s): STA 3386 Fundamental topics of machine learning including supervised/unsupervised learning, cost function optimization, feature selection and engineering, and bias/variance tradeoff  
Learning algorithms including classification methods, support vector machines, decision trees, neural networks, and deep learning are included.
STA 4360  Bayesian Data Analysis  (3)  
Pre-requisite(s): STA 4385 An introduction to Bayesian inference emphasizing prior and posterior distributions, estimation, prediction, hierarchical Bayesian analysis, and applications with computer implemented data analysis  
STA 4362  Applied Time Series Analysis  (3)  
Pre-requisite(s): STA 3386  
Statistical methods of analyzing time series. Model identification, estimation, forecasting, and spectral analysis will be discussed. Applications in a variety of areas including economics and environmental science will be considered.
STA 4370  Sampling Techniques  (3)  
Pre-requisite(s): Three hours of statistical methods  
Planning, execution, and analysis of sampling from finite populations. Simple random, stratified random, ratio, systematic, cluster, sub sampling, regression estimates, and multi-frame techniques are covered.
STA 4371  Data Management and Mining  (3)  
Pre-requisite(s): STA 3381  
Terminology, techniques, and management of Data Mining for biostatisticians.
STA 4372  Introduction to Biostatistics  (3)  
Pre-requisite(s): STA 2381 or STA 3381 or consent of the instructor  
Data Analysis for biostatisticians in the biomedical and pharmaceutical fields.
STA 4373  Computational Methods in Statistics  (3)  
Pre-requisite(s): STA 2381 or STA 3381 or consent of the instructor  
Computational methods using statistical packages and programming.
STA 4374  Statistical Process Control  (3)  
Pre-requisite(s): STA 3381 or equivalent  
Development of statistical concepts and theory underlying procedures used in statistical process control applications and reliability.
STA 4382  Intermediate Statistical Methods  (3)  
Pre-requisite(s): A minimum grade of C in either STA 2381 or STA 3381; or consent of instructor  
Development and application of two-sample inferences, analysis of variance, multiple comparison procedures, and nonparametric methods.
STA 4384  Applied Multivariate Methods  (3)  
Pre-requisite(s): STA 3386  
Numerical and graphical descriptive statistics for multivariate data, principal components and factor analysis, canonical correlation, discriminant analysis, multivariate analysis of variance, multidimensional contingency tables, and cluster analysis.
STA 4385  Mathematical Statistics I  (3)  
Pre-requisite(s): MTH 2321 with minimum grade of C  
Introductions to the fundamentals of probability theory, random variables and their distributions, expectations, transformations of random variables, moment generating functions, special discrete and continuous distributions, multivariate distributions, order statistics, and sampling distributions.
STA 4386  Mathematical Statistics II  (3)  
Pre-requisite(s): STA 4385 with minimum grade of C  
Theory of statistical estimation and hypothesis testing. Topics include point and interval estimation, properties of estimators, properties of test of hypotheses including most powerful and likelihood ratios tests, and decision theory including Bayes and minimax criteria.
STA 4387  Introduction to Probability Models  (3)  
Pre-requisite(s): STA 4385 with minimum grade of C  
Applications of probability theory to the study of phenomena in such fields as engineering, management science, social and physical sciences, and operations research. Topics include Markov chains, branching processes, Poisson processes, exponential models, and continuous-time Markov chains with applications to queuing systems. Other topics introduced are renewal theory and estimation procedures.
STA 43C8  Capstone in Sports Analytics  (3)  
Pre-requisite(s): Senior standing and consent of the instructor  
Applying statistics data science methodology to research problems in sports analytics.
STA 43C9  Capstone Statistics Course  (3)  
Pre-requisite(s): Approval of the statistics undergraduate faculty advisor  
Statistical concepts applied to written and oral reports for consulting. For students majoring in statistics.
STA 4V90  Special Topics in Statistics  (1-3)  
Pre-requisite(s): STA 2381 or STA 3381  
Topics in probability and/or statistics not covered in other courses. May be repeated for a maximum of 6 hours if the content is different.
STA 4V9R  Research  (3)  
Pre-requisite(s): Consent of the instructor  
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.