Revised 7/8/10 - Course syllabi can be found here. 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 practice.

STA 1013 Statistics Through Example (3)

Fundamental concepts of statistics including descriptive measures, randomness, estimation of proportions, central tendency, rare event principle, association versus causation, and risks.

STA 2023 Fundamental Business Statistics (3)

Statistical applications in business, involving graphical and numerical descriptions of data, data collection, elementary probability, random variables, binomial and normal distributions, sampling distributions, and confidence intervals and hypothesis tests for a single example. Prerequisite: MAC 1105 or its equivalent. Special Note: No credit is given for STA 2023 if "C–" or better has been previously earned in STA 2122, 2171, or 3032. 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 data collection, sample variatin, basic probability, confidence intervals, hypothesis testing, analysis of variance, contingency tables, correlation, and regression. Prerequisite: MAC 1105 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. Only two credit hours are given for STA 2122 if a grade of "C–" or better was previously earned in STA 2023 or STA 3014.

STA 2171 Statistics for Biology (4)

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. Prerequisite: MAC 2311 and biology major status or departmental approval. Special Note: Only two semester hours of credit are given for STA 2171 if "C–" or better has been previously earned in STA 2023. No credit is given for STA 2171 if a "C–" or better has been previously earned in STA 2122 or 3032 or QMB 3200.

STA 3024 SAS for Data and Statistical Analyses (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 consent of the instructor.

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 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 consent of instructor.

STA 4173 BIOSTATISTICS (3)

This course introduces students to the statistical methods used to design a, nd 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. Special Note: Subsequent credit for STA 5206 is not permitted.

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. Special Note: Subsequent credit for STA 5207 is not permitted.

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 consent of instructor.

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 Special Note: Credit not given for both STA 4321 and STA 4442.

STA 4322 Mathematical Statistics (3)

Sufficiency, point estimation, confidence intervals, hypothesis testing, regression, linear models, Bayesian analysis. Prerequisite: STA 4321 and MAC 2313 Special Note: Subsequent credit for STA 5325 is not permitted.

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 Special Note: Not open to Statistics majors or minors. Credit not given for both STA 4321 and STA 4442. Subsequent credit for STA 5440 is not permitted.

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 consent of instructor

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: 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 Special Note: Subsequent credit for STA 5707 is not permitted.

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, 2171,QMB 3200 or equivalent. Knowledge of PC's or UNIX. Special Note: Subsequent credit for STA 5856 is not permitted.

STA 4905r Directed Iindividual 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.