Statistics (STA)
Philosophical, ethical, and sociological issues related to statistical uncertainty and randomness.
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.
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.
Principles of data science, including problem workflow, variable types, visualization, modeling, programming, data management and cleaning, reproducibility, and big data.
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.
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.
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.
Concepts in big data analytics primarily applied to topics in sports focusing on graphical methods through dashboards and inferential methods.
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.
Independent study or research in topics not available in other courses. Maximum of four hours will count toward the degree.
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.
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.
Learning algorithms including classification methods, support vector machines, decision trees, neural networks, and deep learning are included.
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.
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.
Terminology, techniques, and management of Data Mining for biostatisticians.
Data Analysis for biostatisticians in the biomedical and pharmaceutical fields.
Computational methods using statistical packages and programming.
Development of statistical concepts and theory underlying procedures used in statistical process control applications and reliability.
Development and application of two-sample inferences, analysis of variance, multiple comparison procedures, and nonparametric methods.
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.
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.
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.
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.
Applying statistics data science methodology to research problems in sports analytics.
Statistical concepts applied to written and oral reports for consulting. For students majoring in statistics.
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.
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.