Undergraduate Course List
The courses offered by the Department encompass three important areas: Applied Statistics, Statistical Theory, and Probability. Furthermore, courses offered by the department are kept up-to-date in accord with latest developments in statistical theory and practices.
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. Requirement Designation LS Stats/Logic.
STA 2023 Fundamental Business Statistics (3)
The 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.
The purpose of this course is to prepare students for further study and job preparation in the field of Business. It will emphasize understanding of data and interpretation of statistical analyses. It will require students to think of data, and report the results of their analyses, in context.
Special Note: High school students who earn a "3" or better on the AP statistics exam will be given credit for STA 2023.
STA 2122 Introduction to Applied Statistics (3)
The 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, non-parametric statistics.
The purpose of this course is to prepare students for further study and job preparation in the field of Natural Sciences. It will emphasize understanding of data and interpretation of statistical analyses. It will require students to think of data, and report the results of their analyses, in context
Prerequisite: A grade of "C-" or better in MAC 1105 College Algebra (or equivalent).
Special Note: Subsequent credit for STA 5126 is not permitted. No credit is 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)
This course provides an introduction to statistics emphasizing applications in 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, analysis of variance and non-parametric tests.
The purpose of this course is to prepare students for further study and job preparation in the field of Biological Sciences including Medicine, Dentistry, other healthcare professions, Veterinary Medicine, Zoology and Botany. It will emphasize understanding of data and interpretation of statistical analyses. It will require students to think of data, and report the results of their analyses, in context.
Prerequisite: MAC 2311 Calculus I and Biology major status, or departmental approval.
Special Note: No credit is given for STA 2171 if a "C-" or better has been previously earned in STA 2122 or STA 3032 or QMB 3200.
STA 3024 SAS for Data and Statistical Analysis (3)
This course covers linear and multiple regression; one-and-two-way analysis of variance; chi-square and contingency tables; design, analysis, evaluation and interpretation of statistical models. Well-prepared students can skip STA 3024 and take either STA 4202 or 4203.
Prerequisite: Introductory statistics course at or above the 2000 level or instructor permission.
STA 3032 Probability and Statistics for Scientists and Engineers (3-5)
This course will cover 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.
Prerequisite: MAC2312
STA 3064 Introduction to Statistical Modeling with SAS (3)
This course is a sequel to STA 3024, SAS for Data and Statistical Analyses. We will cover the following topics utilizing the SAS software: ANOVA, Linear Modeling, Logistic Regression, bootstrap sampling, simulation using the data step, and some additional topics in the data step.
Prerequisite: STA 3024 or instructor permission.
STA 3732. Statistical Tools for Data Analytics (3).
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.
Prerequisite: STA 3024 or instructor permission.
STA 4102 Computational Methods in Statistics I (3)
Matlab and a programming language (C/Fortran) will be used. Floating point arithmetic, numerical matrix analysis, multiple regression analysis, non-linear optimization, root finding, numerical integration, Monte Carlo sampling, survey of density estimation.
Prerequisite: At least one statistics above STA 1013, some programming experience, or instructor permission.
STA 4103 Computational Methods in Statistics II (3)
Matlab and a programming language (C/Fortran) will be used. A continuation of STA 4102 in computational techniques for linear and non-linear statistics. Statistical image understanding, elements of pattern theory, simulated annealing, Metropolis-Hastings algorithm, Gibbs sampling.
Prerequisite: STA 4102 or instructor permission.
STA 4173 Biostatistics (3)
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)
One- and two-way classifications, nesting, blocking, multiple comparisons, incomplete designs, variance components, factorial designs, confounding.
Prerequisite: STA 2122, STA 2171, STA 3032, or QMB 3200.
STA 4203 Applied Regression Methods (3)
General linear hypothesis, multiple correlation and regression, residual analysis and model identification.
Prerequisite: STA 2122, 2171, 3032, 4322 or QMB 3200.
STA 4222 Sample Surveys (3)
Simple, stratified, systematic, and cluster random sampling. Ratio and regression estimation, multistage sampling.
Prerequisite: a course in statistics above STA 1013, or instructor permission.
STA 4321 Introduction to Mathematical Statistics (3)
Distribution of random variables, conditional probability and independence, multivariate distributions, sampling distributions, Bayes' rule, counting problems, expectations.
Prerequisite: MAC 2313
STA 4322 Mathematical Statistics (3)
Sufficiency, point estimation, confidence intervals, hypothesis testing, regression, linear models, Bayesian analysis.
Prerequisite: STA 4321 and MAC 2313
STA 4442 Introductory Probability I (3)
Random variables, probability distributions, independence, sums of random variables, generating functions, central limit theorem, laws of large numbers.
Prerequisite: MAC 2312
STA 4502 Nonparametric Methods (3)
Application of nonparametric tests, estimates, confidence intervals, and multiple comparison procedures.
Prerequisite: a course in statistics above STA 1013 or instructor permission.
STA 4634 Applied Machine Learning (3)
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.
Prerequisite: STA 3032 or instructor permission.
STA 4664 Statistics for Quality and Productivity (3)
Deming's ideas, graphical methods, control charts, and design of experiments for product and process improvement.
Prerequisite: STA 4322 or instructor permission, as well as STA 2122 or STA 2171 or STA 3032 or STA 4442.
STA 4702 Applied Multivariate Analysis (3)
Principal components and factor analysis, canonical correlation, discriminant analysis, multivariate analysis of variance, multidimensional contingency tables, cluster analysis.
Prerequisite: STA 4203 or 4322
STA 4853 Time Series and Forecasting Methods (3)
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.
Prerequisite: STA 2122, STA 2171, QMB 3200 or equivalent. Knowledge of PCs or UNIX.
STA 4905r Directed Individual Study (2-3)
(S/U grade only). Repeatable to a maximum of 12 semester hours.
STA 4930r Selected Topics in Statistics, Probability, or Operations Research (2-3)
Repeatable to a maximum of 12 semester hours.
STA 4931 Statistics in Practice (3)
This is a capstone course intended for statistics majors. The goal will be to enhance students' competencies by applying advanced statistical methodology to the challenges imposed by real data and developing effective writing skills to effectively communicate project requirements and findings. Students will be exposed to several aspects of statistical practices including elements of statistical consulting, study design, setting project goals and deliverables, applying appropriate methodology, performing accurate analyses, and providing clear and concise explanations of results. Fulfills upper division writing requirement.
Prerequisite: Two 4000 level statistics courses. Reserved for statistics majors only.