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Statistics (STA) Course Descriptions

STA 5073  Methods of Statistics
(3-0) 3 hours credit. Prerequisite: STA 1053.
Emphasis on methods and applications of statistics. Measure of location, variability, and association. Interpretation of categorical data, hypothesis testing, and use of SAS programs and applications. Cannot be applied to a Master of Science degree in Applied Statistics.

STA 5093  Introduction to Statistical Inference
(3-0) 3 hours credit. Prerequisite: Admission to the M.S. program or consent of instructor.
Introduction to experiments and sampling; probability, random variables, and distributions; standard discrete and continuous models; sampling distributions; maximum likelihood and moment estimation; confidence intervals and hypothesis tests for one- and two-sample means, proportions, and variances; large sample and bootstrap methods; goodness-of-fit and nonparametric tests. Use of R for simulation and inference.

STA 5103  Applied Statistics
(3-0) 3 hours credit. Prerequisite: STA 5093 or consent of instructor.
Simple linear regression, correlation, multiple regression, model selection, one-, and two-way analysis of variance, fixed-, random- and mixed-effects models, multiple comparisons, factorial experiments, and logistic regression. Use of statistical packages such as SAS or JMP for data analysis.

STA 5133  Advanced Programming and Data Management in SAS
(3-0) 3 hours credit. Prerequisite: An introductory course in computer programming or consent of instructor.
Essential SAS programming concepts with a focus on data management and the preparation of data for statistical analysis: reading raw data from different sources, creating data files in various formats, creating and modifying SAS datasets, SAS libraries, formats, character and numeric functions, combining datasets, summarizing and displaying data, arrays and macros. Efficient programming techniques are stressed.

STA 5253  Time Series Analysis and Applications
(3-0) 3 hours credit. Prerequisite: STA 5513 or consent of instructor.
Examples and goals of time series analysis, autocovariance function, stationarity, linear processes, autoregressive and moving average (ARMA) processes, spectral analysis, the periodogram, linear filters, regression models with ARMA errors, forecasting in times series models, estimation by maximum likelihood and least squares, diagnostics, model selection, autoregressive integrated moving average (ARIMA) and other nonstationary processes.

STA 5313  Theory of Sample Surveys with Applications
(3-0) 3 hours credit. Prerequisite: STA 5093 or consent of instructor.
Basic sampling techniques and their comparisons for finite populations. Topics include simple random sampling, stratified sampling, ratio and regression estimates, systematic sampling, cluster sampling, multistage and double sampling, and bootstrap and other sampling plans.

STA 5413  Nonparametric Statistics
(3-0) 3 hours credit. Prerequisite: STA 5093 or consent of instructor.
Order statistics, test of goodness of fit, rank-order statistics, linear rank statistics for problems involving location and scale, association in multiple classifications, and asymptotic relative efficiency.

STA 5503  Mathematical Statistics I
(3-0) 3 hours credit. Prerequisite: Admission to the Statistics graduate program or consent of instructor.
Axioms of probability, counting rules, univariate random variables, multivariate random variables, joint, marginal, and conditional probability distributions, mathematical expectation, variable transformation, moment generating function, commonly used probability distributions, sampling distributions, laws of large numbers and the central limit theorem.

STA 5513  Mathematical Statistics II
(3-0) 3 hours credit. Prerequisite: STA 5503 or consent of instructor.
Data reduction, sufficient and complete statistics, unbiased estimation, maximum likelihood estimation, method of moments, best unbiased estimator, Fisher information, Cramer-Rao lower bound, hypothesis testing, likelihood ratio test, Neyman-Pearson lemma and uniformly most powerful test, and interval estimation.

STA 5803  Process Control and Acceptance Sampling
(3-0) 3 hours credit. Prerequisite: STA 5093 or consent of instructor.
Introduction to statistical process control and product inspection plans. Topics include control charts by attributes and variables, special control charts, specification limits, process capability, and acceptance sampling plans by attributes and variables. Use of statistical software.

STA 5903  Survival Analysis
(3-0) 3 hours credit. Prerequisite: STA 5093 or consent of instructor.
This course introduces both parametric and nonparametric methods for analyzing survival data. Topics include Kaplan-Meier estimator, inference based on standard lifetime distributions, regression approach to survival analysis including the Cox proportional hazards model. Emphasis on application and data analysis using SAS and S-Plus.

STA 5973  Directed Research
3 hours credit. Prerequisites: Graduate standing and permission in writing (form available) of the instructor and the student’s Graduate Advisor of Record.
The directed research course may involve either a laboratory or a theoretical problem. May be repeated for credit, but not more than 6 hours, regardless of discipline, will apply to the Master’s degree.

STA 6013  Regression Analysis
(3-0) 3 hours credit. Prerequisite: STA 5103 or consent of instructor.
Model selection methods, model validation, diagnostics, outlier detection, autocorrelated data, multicollinearility, cross validation, transformation of data, and generalized linear regression models.

STA 6023  Mathematical Methods in Statistics
(3-0) 3 hours credit. Prerequisite: STA 5103 or consent of instructor.
This course provides mathematical methods used in various branches of statistics. The topics include: 1) Linear Algebra: systems of linear equations, matrix algebra, quadratic forms, vector spaces and linear independence, eigenvalues and eigenvectors, and matrix factorizations; and 2) Large Sample Theory: convergence of sequence, series, sequence and series of functions, Taylor series expansions, multivariate functions, gradient vector and Hessian matrix, and extremes of multivariate functions.

STA 6113  Applied Bayesian Statistics
(3-0) 3 hours credit. Prerequisite: STA 5513 or consent of instructor.
Probability and uncertainty, conditional probability and Bayes’ Rule, single parameter and multiple parameter Bayesian analysis, posterior analysis for commonly used distributions, prior distribution elicitation, Bayesian methods in linear models, Bayesian computation including Markov chain Monte Carlo (MCMC) simulation, and applications.

STA 6133  Simulation and Statistical Computing
(3-0) 3 hours credit. Prerequisite: STA 5513 or consent of instructor.
Random variable generation, accept-reject methods, simulation from multivariate distributions, Markov chain Monte Carlo simulation, numerical quadrature, Monte Carlo integration, importance sampling, Laplace approximation, methods for variance reduction, bootstrap and jackknife, deterministic methods for function optimization, and EM algorithm.

STA 6233  Advanced Statistical Programming Using SAS Software
(3-0) 3 hours credit. Prerequisites: STA 5103 and STA 5133.
Methods for analyzing continuous and categorical data, using Base SAS, SAS/Graph and SAS/STAT software modules. Applications are drawn from regression analysis, analysis of variance, categorical data analysis, survival analysis multivariate methods, simulation and resampling. Implementation of methods, efficient programming, and interpretation of results are the focus of a written project or oral presentation.

STA 6713  Linear Models
(3-0) 3 hours credit. Prerequisite: STA 5103 or consent of instructor.
Multivariate normal distribution; distribution of quadratic forms; Gauss Markov Theorem; theory for the full rank and less than full rank models; generalized least squares; estimability and testable hypotheses; general linear hypothesis; linear mixed models and variance components; generalized linear models. (Formerly STA 5713. Credit can be earned for only one of the following: STA 5713, STA 6713, or STA 7723.)

STA 6813  Multivariate Analysis
(3-0) 3 hours credit. Prerequisite: STA 5093 or consent of instructor.
Multivariate normal distribution; estimation of mean vector and covariance matrix; Hotelling’s T2; principal components, factor analysis, MANOVA, multivariate regression; cluster analysis, discriminant analysis; Wishart distribution; and tests concerning covariance matrices.

STA 6833  Design and Analysis of Experiments
(3-0) 3 hours credit. Prerequisite: STA 5103 or consent of instructor.
Introduction to experimental design and data analysis in scientific and engineering settings. Topics include one- and two-factor experiments, randomized block designs, two- and three-level factorial and fractional factorial designs, nested and split-plot designs, and optimal designs. Use of statistical software such as SAS for data analysis. (Formerly STA 5833. Credit cannot be earned for both STA 6833 and STA 5833.)

STA 6843  Response Surface Methodology
(3-0) 3 hours credit. Prerequisite: STA 6833 or consent of instructor.
Factorial designs, first and second order models, process improvement with steepest ascent, experimental designs for fitting response surfaces, use of model diagnostics for finding optimum operating conditions, and robust parameter designs.

STA 6853  Categorical Data Analysis
(3-0) 3 hours credit. Prerequisite: STA 5093 or consent of instructor.
Types of categorical data, analysis of cross-classified tables, test of independence, measures of association, logit models and analogies with regression, multinomial logit models, log-linear models for two- and multi-dimensional tables, specialized methods for ordinal data, and models for matched pairs data, delta method and large sample tests. Use of statistical packages such as SAS for data analysis.

STA 6863  Spatial Statistics
(3-0) 3 hours credit. Prerequisite: STA 5103 or consent of instructor.
Problems dealing with spatial statistics, random fields, Gaussian random fields, covariograms and variograms, stationarity and isotropy, covariogram/variogram estimation, spatial prediction (kriging), statistical properties of kriging predictors, cross validation, simulation of random fields, models for lattice/areal data.

STA 6913  Bioinformatics: Microarray and Proteomics Data Analysis
(3-0) 3 hours credit. Prerequisite: STA 5103 or consent of instructor.
This course provides a detailed overview of statistical methods used in microarray and proteomics data analysis and exploits the design of such experiments. The topics include introduction to genome biology and microarray technology, R programming and Bioconductor, pre-processing, normalization, microarray experimental design and analysis, multiple testing, LIMMA, dimension reduction in microarray, cluster analysis, and classification in microarray experiments. (Formerly STA 5913. Credit cannot be earned for both STA 6913 and STA 5913.) (Formerly titled “Bioinformatics and Data Mining I: Microarray Data Analysis.”)

STA 6923  Advanced Statistical Learning/Data Mining
(3-0) 3 hours credit. Prerequisite: STA 5103 or consent of instructor.
This course provides an overview of statistical learning and data mining tools in analyzing the vast amounts of data found in bioinformatics, business, and other high-tech industries. The topics include R programming, data gathering and cleansing, linear models, generalized additive models, model assessment, Classification and Regression Trees (CART), bagging and boosting, random forest, neural networks, support vector machines, nearest-neighbor classification, combining classifiers, cluster analysis, association rules, visualization, and applications to microarray/proteomics data analysis. (Formerly STA 5923 and STA 7923. Credit can be earned for only one of the following: STA 5923, STA 6923, or STA 7923.)

STA 6943  Statistics Internship
3 hours credit. Prerequisites: Graduate standing, 15 semester credit hours of graduate work, and consent of instructor. Internship must be approved in advance by the Internship Coordinator and the student’s Graduate Advisor of Record.
Supervised full- or part-time off-campus work experience and training in statistics. Individual conferences and written reports required.

STA 6953  Independent Study
3 hours credit. Prerequisites: Graduate standing and permission in writing (form available) of the instructor and the student’s Graduate Advisor of Record.
Independent reading, research, discussion, and/or writing under the direction of a faculty member. For students needing specialized work not normally or not often available as part of the regular course offerings. May be repeated for credit, but not more than 6 hours, regardless of discipline, will apply to the degree.

STA 6961  Comprehensive Examination
1 hour credit. Prerequisite: Approval of the appropriate Graduate Program Committee to take the Comprehensive Examination.
Independent study course for the purpose of taking the Comprehensive Examination. May be repeated as many times as approved by the Graduate Program Committee. Enrollment is required each term in which the Comprehensive Examination is taken if no other courses are being taken that term. The grade report for the course is either “CR” (satisfactory performance on the Comprehensive Examination) or “NC” (unsatisfactory performance on the Comprehensive Examination).

STA 6971-3  Special Problems
(1-0, 2-0, 3-0) 1 to 3 hours credit. Prerequisite: Consent of instructor.
An organized course offering the opportunity for specialized study not normally or not often available as part of the regular course offerings. Special Problems courses may be repeated for credit when topics vary, but not more than 6 hours, regardless of discipline, will apply to the degree.

STA 6983  Master’s Thesis
3 hours credit. Prerequisites: Permission of the Graduate Advisor of Record and thesis director.
Thesis research and preparation. May be repeated for credit, but not more than 6 hours will apply to the Master’s degree. Credit will be awarded upon completion of the thesis. Enrollment is required each term in which the thesis is in progress.

STA 6991  Statistical Consulting
(1-0) 1 hour credit. Prerequisites: Background in regression analysis and experimental design. Restricted to students who have completed two semesters in the Master’s or Doctoral programs.
The principles dealing with the basic art and concepts of consulting in statistics. This course discusses the roles and responsibilities of applied statisticians, relationship between clients and consultants, effective information gathering and report writing. Each student is assigned at least one consulting problem and is required to submit a comprehensive final report. May be repeated for credit, but not more than 3 hours can be applied to the Doctoral degree.

STA 7013  Advanced Applied Business Statistical Methods
(3-0) 3 hours credit. Prerequisite: Consent of instructor.
The course will focus on the applications of statistical methods in business. Topics include basic probability theory, models for discrete and continuous data, sampling distributions, confidence intervals for means and proportions, hypothesis tests for means, proportions, and variances, goodness-of-fit tests, power of tests and sample size determination, and nonparametric statistical techniques. Emphasis will be placed on understanding the underlying assumptions and limitations of the different techniques. Statistical computer software such as SPSS or SAS will be used in the course for data analysis. This course is designed for doctoral students in Business and cannot be applied to a Master of Science degree in Applied Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

STA 7023  Applied Linear Statistical Models
(3-0) 3 hours credit. Prerequisite: Consent of instructor.
An in-depth study of regression and analysis of variance models. Topics include multiple regression and model building, multiple and partial correlation, analysis of residuals, analysis of variance, multivariate analysis of variance, analysis of variance as regression analysis, generalized linear model, and applications of statistical models to problems in business. Computer software packages such as SAS or SPSS will be used for data analysis. This course is designed for doctoral students in Business and cannot be applied to a Master of Science degree in Applied Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

STA 7033  Multivariate Statistical Analysis
(3-0) 3 hours credit. Prerequisite: Consent of instructor.
An advanced treatment of multivariate statistical techniques. Topics include multivariate normal distribution, multivariate tests of hypotheses, confidence regions, principal component analysis, factor analysis, discrimination and classification analysis, and clustering. Computer software packages such as SAS or SPSS will be used for data analysis. This course is designed for doctoral students in Business and cannot be applied to a Master of Science degree in Applied Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

STA 7083  Time Series Analysis
(3-0) 3 hours credit. Prerequisite: Consent of instructor.
Univariate and multivariate time series analysis of economic and financial data, autoregressive integrated moving average (ARIMA) models and vector autoregression, out-of-sample forecasting using computer software. Unit roots, cointegration and error correction, and ARCH models. This course is designed for doctoral students in Business and cannot be applied to a Master of Science degree in Applied Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record. (Credit cannot be earned for both STA 7083 and STA 7043. Same as ECO 7083. Credit cannot be earned for both ECO 7083 and STA 7083.)

STA 7113  Bayesian Statistics
(3-0) 3 hours credit. Prerequisite: STA 6113 or consent of instructor.
Topics include multiple parameter Bayesian analysis, informative and objective Bayesian methods, Bayesian and frequentist interface, Bayesian variable selection and model averaging, Bayesian hierarchical models and empirical models, Bayesian model checking, Bayesian applications to generalized linear models, and Bayesian decision theory.

STA 7211-6  Doctoral Research
1 to 6 hours credit.
May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7311-6  Doctoral Dissertation
1 to 6 hours credit. Prerequisite: Admission to candidacy for Doctoral degree in Applied Statistics.
May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7503  Advanced Inference I
(3-0) 3 hours credit. Prerequisites: STA 5503 and STA 5513 or equivalent and Doctoral standing.
Brief introduction to measure and Lebesgue integration, location-scale families of distributions, exponential families of distributions, sufficiency, completeness, ancillarity, Fisher information, model identifiability, principles of estimation, best unbiased estimation, variance lower bounds, maximum likelihood estimation, and small sample properties of estimators.

STA 7513  Advanced Inference II
(3-0) 3 hours credit. Prerequisite: STA 7503.
Different forms of stochastic convergence, laws of large numbers, central limit theorems, multivariate delta method, asymptotic properties of maximum likelihood estimators, tests of hypotheses, Neyman-Pearson theory, uniformly most powerful tests, unbiased tests, monotone likelihood ratio families, likelihood ratio tests, Wald and Rao/Score tests, asymptotic properties of tests, tests of linear hypothesis, Bonferroni and Scheffe multiple tests, confidence regions, duality between confidence regions and tests of hypotheses.

STA 7903  Advanced Survival Analysis: Counting Process Approach
(3-0) 3 hours credit. Prerequisite: Consent of instructor. STA 5903 recommended.
This course introduces and extends the survival regression model to multiple event data using a counting process approach. The topics include counting processes, estimation of the survival and hazard functions, Cox model, residual and influence analysis, testing proportional hazard, multiple events model, frailty models, and R programming.

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