2026-2027 Academic Catalog

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Statistics (STAT)

STAT 1004 - The First Year Experience in Learning from Data (2 credits) 
Introduction to the field of statistics and aspects of college life for first year students. Topics included: history of the statistics; key roles of statisticians in field, such as actuarial sciences, pharmaceutical, medical, and bioinformatics industries, governmental agencies, academia; fundamental principles of statistical fields of study and applications; exploring data sets; and aspects of college life for first-year students.
Instructional Contact Hours: (2 Lec, 2 Crd) 
STAT 1014 - Data in Our Lives (3 credits) 
Develop and practice the process of thinking critically with data in the context of real world problems. Import, manage, summarize, and visualize data using programmable, statistical software. Make data discoveries, make decisions, generate hypotheses, and/or communicate findings in data. Consider laws of probability and personal biases to weigh decisions. Recognize ethical issues and vulnerabilities in analyses when learning from data and extrapolating to large populations.
Pathway Concept Area(s): 5F Quant & Comp Thnk Found., 10 Ethical Reasoning 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 1984 - Special Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 2004 - Introductory Statistics (3 credits) 
Fundamental concepts and methods of statistics with emphasis on interpretation of statistical arguments and statistical reasoning. Using modern, accessible statistical software and technology, an introduction to design of experiments (including data collection), data analysis, data visualization, correlation and regression, concepts of probability theory, sampling errors, confidence intervals, and hypothesis tests. Include real-world applications to develop problem-solving skills and consider ethical implications within the context of learning from data. No credit will be given for 2004 if taken with or after any other statistics course, except STAT 2984.
Pathway Concept Area(s): 5F Quant & Comp Thnk Found., 10 Ethical Reasoning 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 2094 - Basic R for Statistics (1 credit) 
Introduction to R/RStudio programming techniques with an emphasis on basic statistical visualizations, descriptive and summary statistics, and elementary inferential statistics. Topics include data types, data structures, importing/exporting, and manipulating datasets, functions, packages, and RMarkdown.
Instructional Contact Hours: (1 Lec, 1 Crd) 
STAT 2194 - Basic SQL for Statistics (1 credit) 
Introduction to Structured Query Language (SQL) for accessing, managing, and analyzing data in relational databases. Topics include database concepts, data types, filtering and summarizing data, joins across multiple tables, handling missing and duplicate data, and basic data manipulation using SQL functions. Students will apply SQL to extract datasets for basic statistical analysis and integrate the resulting data into statistical computing environments. Emphasis is placed on practical data querying skills relevant to statistics, working with real-world data.
Instructional Contact Hours: (1 Lec, 1 Crd) 
STAT 2274 - Basic Python For Statistics (1 credit) 
Use of Python code and libraries (SciPy and NumPy) to support basic statistical tasks, create graphical displays, and perform statistical inference and hypothesis tests to evaluate datasets. Use of editors and AI to generate Python code.
Instructional Contact Hours: (1 Lec, 1 Crd) 
STAT 2704 - Data Playbook: Analyzing Sports Statistics (3 credits) 
Sports analytics course is designed for students to learn the rules and structures of these sports and how they relate to game statistics. The course covers basic descriptive and performance statistics, sports data visualization, probability, sports betting and odds, and regression analysis. Emphasis is placed on understanding how statistics enhance communication in sports media and the ethical considerations of sports gambling. No statistical background is required.
Pathway Concept Area(s): 5A Quant & Comp Thnk Adv., 10 Ethical Reasoning 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 2964 - Field Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 2974H - Independent Study (1-19 credits) 
Honors section.
Instructional Contact Hours: Variable credit course 
STAT 2984 - Special Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 2984O - Special Study (1-19 credits) 
Pathway Concept Area(s): 5A Quant & Comp Thnk Adv. 
Instructional Contact Hours: Variable credit course 
STAT 3005 - Statistical Methods (3 credits) 
Introduction to statistical methods for analyzing real-world datasets with emphasis on ethical practice in data science. STAT 3005: Descriptive statistics, data visualization, sampling methods, experimental design, probability and probability distributions, statistical inference (confidence intervals and hypothesis testing), simple linear regression, Analysis of Variance (ANOVA), and chi-squared tests. STAT 3006: One-way and two-way ANOVA, multiple comparisons, simple and multiple linear regression, frequency data analysis (chi-square tests), generalized linear regression models, and nonparametric procedures. STAT 3005 duplicates STAT 3615 and STAT 4604. Credit will only be awarded for completion of one. STAT 3006 duplicates STAT 3616, STAT 4604, and STAT 4706. Credit will only be awarded for completion of one.
Prerequisite(s): MATH 1225 
Pathway Concept Area(s): 5A Quant & Comp Thnk Adv., 10 Ethical Reasoning 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 3006 - Statistical Methods (3 credits) 
Introduction to statistical methods for analyzing real-world datasets with emphasis on ethical practice in data science. STAT 3005: Descriptive statistics, data visualization, sampling methods, experimental design, probability and probability distributions, statistical inference (confidence intervals and hypothesis testing), simple linear regression, Analysis of Variance (ANOVA), and chi-squared tests. STAT 3006: One-way and two-way ANOVA, multiple comparisons, simple and multiple linear regression, frequency data analysis (chi-square tests), generalized linear regression models, and nonparametric procedures. STAT 3005 duplicates STAT 3615 and STAT 4604. Credit will only be awarded for completion of one. STAT 3006 duplicates STAT 3616, STAT 4604, and STAT 4706. Credit will only be awarded for completion of one.
Prerequisite(s): STAT 3005 or STAT 4705 or CMDA 2005 or STAT 3615 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 3094 - SAS Programming (3 credits) 
Introduction to basic programming techniques: creating DATA and PROC statements, libraries, functions, programming syntax and formats. Other topics include loops, SAS Macros and PROC IML. Emphasis is placed on using these tools for statistical analyses. The pre-requisite may be substituted for an equivalent course.
Prerequisite(s): STAT 3005 or CMDA 2006 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 3104 - Probability and Distributions (3 credits) 
Probability theory, including set theoretic and combinatorial concepts; in-depth treatment of discrete random variables and distributions, with some introduction to continuous random variables; introduction to estimation and hypothesis testing.
Prerequisite(s): (MATH 1226 or MATH 1026) and (STAT 3005 or STAT 3615 or STAT 4705 or CMDA 2005) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 3204 - Data Visualization (3 credits) 
Using quantitative and qualitative thinking to develop a working knowledge of data visualization considerations, methods and techniques that lead to: understanding the audience(s); creating ethical data stories; data visualization as a method of storytelling; ethical and appropriate data exploration, manipulation, and cleaning; design considerations; types of visualizations; tools and resources for creating visualizations.
Prerequisite(s): (STAT 1014 or STAT 2004 or STAT 3005 or STAT 3604 or STAT 3615 or STAT 4705 or STAT 4706 or STAT 4714 or CMDA 2005 or CMDA 2014) and (COMM 1016 or ENGL 1105) 
Pathway Concept Area(s): 5A Quant & Comp Thnk Adv., 10 Ethical Reasoning 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 3274 - Introduction to Sports Analytics Research (3 credits) 
Introduction to sports analytics, sources of sports analytics data and data collection methods, visualization techniques, game performance statistics, inferential statistics and predictive modeling techniques for sports data. Role and applications of data analytics in the sports industry.
Prerequisite(s): CMDA 2006 or STAT 3006 
Corequisite(s): CMDA 3654 or CS 3654 or STAT 3654. 
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: CMDA 3274 
STAT 3504 - Nonparametric Statistics (3 credits) 
Statistical methodology based on ranks, empirical distributions, and runs. One and two sample tests, ANOVA, correlation, goodness of fit, and rank regression, R-estimates and confidence intervals. Comparisons with classical parametric methods. Emphasis on assumptions and interpretation.
Prerequisite(s): STAT 3006 or STAT 3616 or STAT 4106 or STAT 4604 or STAT 4706 or CMDA 2006 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 3604 - Statistics for Social Science (3 credits) 
Statistical methods for nominal, ordinal, and interval levels of measurement. Topics include descriptive statistics, elements of probability, discrete and continuous distributions, one and two sample tests, measures of association. Emphasis on comparison of methods and interpretations at different measurement levels. Includes real-world applications to develop problem-solving skills and ethical reasoning within the context of learning from data.
Prerequisite(s): MATH 1014 or MATH 1025 or MATH 1214 or MATH 1225 or MATH 1524 
Pathway Concept Area(s): 5A Quant & Comp Thnk Adv., 10 Ethical Reasoning 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 3615 - Biological Statistics (3 credits) 
Statistical methods for the analysis of experimental and observational data with emphasis on biological applications, communicating results, and ethical practices. 3615: Topics include descriptive statistics, data visualization, sampling methods, experimental design, probability and probability distributions, statistical inference (confidence intervals and hypothesis testing), simple linear regression, Analysis of Variance (ANOVA), and chi-squared tests. 3616: Topics include one-way and two-way ANOVA, multiple comparisons, simple and multiple linear regression, frequency data analysis (chi-square tests), generalized linear regression models, and nonparametric procedures. STAT 3615 duplicates STAT 3005 and STAT 4604. Only one may be taken for credit. STAT 3616 duplicates STAT 3006, STAT 4604, and STAT 4706. Only one may be taken for credit.
Prerequisite(s): MATH 1225 or MATH 1025 or MATH 1524 or ISC 1105 
Pathway Concept Area(s): 5A Quant & Comp Thnk Adv., 10 Ethical Reasoning 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 3616 - Biological Statistics (3 credits) 
Statistical methods for the analysis of experimental and observational data with emphasis on biological applications, communicating results, and ethical practices. 3615: Topics include descriptive statistics, data visualization, sampling methods, experimental design, probability and probability distributions, statistical inference (confidence intervals and hypothesis testing), simple linear regression, Analysis of Variance (ANOVA), and chi-squared tests. 3616: Topics include one-way and two-way ANOVA, multiple comparisons, simple and multiple linear regression, frequency data analysis (chi-square tests), generalized linear regression models, and nonparametric procedures. STAT 3615 duplicates STAT 3005 and STAT 4604. Only one may be taken for credit. STAT 3616 duplicates STAT 3006, STAT 4604, and STAT 4706. Only one may be taken for credit.
Prerequisite(s): STAT 3615 or STAT 3005 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 3654 - Introductory Data Analytics and Visualization (3 credits) 
Basic principles and techniques in data analytics; methods for the collection of, storing, accessing, and manipulating standard-size and large datasets; data visualization; and identifying sources of bias.
Prerequisite(s): (CS 1114 or CS 1044 or CS 1054 or CS 1064) and (MATH 2204 or MATH 2204H or MATH 2406H or CMDA 2005) and (STAT 3006 or STAT 4105 or STAT 4705 or STAT 4714 or CMDA 2006) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: CMDA 3654, CS 3654 
STAT 3704 - Statistics for Engineering Applications (2 credits) 
Introduction to statistical methodology with emphasis on engineering experimentation: probability distributions, estimation, hypothesis testing, regression, and analysis of variance. Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit.
Prerequisite(s): MATH 2224 or MATH 2224H or MATH 2204 or MATH 2204H or MATH 2406H or CMDA 2005 
Instructional Contact Hours: (2 Lec, 2 Crd) 
STAT 3984 - Special Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 4004 - Methods of Statistical Computing (3 credits) 
Computationally intensive computer methods used in statistical analyses. Statistical univariate and multivariate graphics; resampling methods including bootstrap estimation and hypothesis testing and simulations; classification and regression trees; scatterplot smoothing and splines.
Prerequisite(s): STAT 4105 and STAT 4214 
Instructional Contact Hours: (4 Lec, 3 Crd) 
STAT 4024 - Communication in Statistical Collaborations (3 credits) 
Theory and examples of effective communication in the context of statistical collaborations. Practice developing the communication skills necessary to be effective statisticians using peer feedback and self-reflection. Topics include helping scientists answer their research questions, writing about and presenting statistical concepts to a non-statistical audience, and managing an effective statistical collaboration meeting. Senior standing in the Department of Statistics.
Prerequisite(s): STAT 4214 and STAT 4204 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4094 - Advanced R for Statistics (1 credit) 
Builds upon foundational R programming skills, focusing on advanced data analysis, visualization, and package development techniques. Topics include an in-depth exploration of the tidyverse for data wrangling, advanced function creation, debugging, and profiling code, building and documenting R packages and generating advanced visualizations, including interactive and 3D plots. Students will enhance their ability to write efficient, reusable, and robust R code to solve advanced statistical problems.
Prerequisite(s): STAT 2094 or STAT 3006 or CMDA 2006 
Instructional Contact Hours: (1 Lec, 1 Crd) 
STAT 4105 - Theoretical Statistics (3 credits) 
4105: Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 4106: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance. STAT 4105 partially duplicates STAT 4705, STAT 4714, and STAT 4724, only one may be taken for credit.
Prerequisite(s): (MATH 2204 or MATH 2204H or CMDA 2005 or MATH 2406H) and (STAT 3104 or CMDA 2006) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4106 - Theoretical Statistics (3 credits) 
4105: Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 4106: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance. STAT 4105 partially duplicates STAT 4705, STAT 4714, and STAT 4724, only one may be taken for credit.
Prerequisite(s): STAT 4105 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4204 - Experimental Designs (3 credits) 
Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Discussion of completely randomized, randomized complete block, and Latin square designs, analysis of covariance, split--plot designs, factorial and fractional designs, incomplete block designs.
Prerequisite(s): STAT 3006 or STAT 3616 or STAT 4106 or STAT 4706 or STAT 5605 or STAT 5615 or CMDA 2006 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4214 - Methods of Regression Analysis (3 credits) 
Multiple regression including variable selection procedures; detection and effects of multicollinearity; identification and effects of influential observations; residual analysis; use of transformations. Non-linear regression, the use of indicator variables, and logistic regression. Use of SAS.
Prerequisite(s): STAT 3006 or STAT 3616 or STAT 4106 or STAT 4706 or STAT 5606 or STAT 5616 or CMDA 2006 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4274 - Sports Analytics Statistical Research (3 credits) 
Statistical analysis of sports data. Game performance statistics and expected scores. Analysis of player performance, player tracking, team performance, and sports betting. Bayesian methods and prediction models applied to sports data. Decision-making. Assessing sports analytics research and literature.
Prerequisite(s): (STAT 4214 or CMDA 4654 or CS 4654 or STAT 4654) and (STAT 3274 or CMDA 3274) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: CMDA 4274 
STAT 4364 - Introduction to Statistical Genomics (3 credits) 
Statistical methods for bioinformatics and genetic studies, with an emphasis on statistical analysis, assumptions, and problem-solving. Topics include: commonly used statistical methods for gene identification, association mapping and other related problems. Focus on statistical tools for gene expression studies and association studies, multiple comparison procedures, likelihood inference and preparation for advanced study in the areas of bioinformatics and statistical genetics.
Prerequisite(s): (MATH 2224 or MATH 2224H or MATH 2204 or MATH 2204H or MATH 2406H or CMDA 2005) and (STAT 3104 or STAT 4105 or STAT 4705 or CMDA 2006) and (STAT 3006 or STAT 3616 or STAT 4706 or CMDA 2006) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4444 - Applied Bayesian Statistics (3 credits) 
Introduction to Bayesian methodology with emphasis on applied statistical problems: data displaying, prior distribution elicitation, posterior analysis, models for proportions, means and regression.
Prerequisite(s): (MATH 2224 or MATH 2224H or MATH 2204 or MATH 2204H or MATH 2406H or CMDA 2005) and (STAT 3104 or STAT 4105 or STAT 4705 or CMDA 2006) and STAT 3006 or STAT 3616 or STAT 4706 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4504 - Applied Multivariate Analysis (3 credits) 
Non-mathematical study of multivariate analysis. Multivariate analogs of univariate test and estimation procedures. Simultaneous inference procedures. Multivariate analysis of variance, repeated measures, inference for dispersion and association parameters, principal components analysis, discriminate analysis, cluster analysis. Use of SAS.
Prerequisite(s): STAT 3006 or STAT 4706 or CMDA 2006 or STAT 3616 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4514 - Introduction to Categorical Data Analysis (3 credits) 
Statistical approaches to analyze categorical data. Probability computation and distribution specification, interval estimation and hypothesis testing, formulating and fitting generalized linear models including logistic and Poisson regression, algorithms used for model fitting, variable selection, and classification trees and supervised learning.
Prerequisite(s): STAT 3006 or STAT 3616 or STAT 4106 or STAT 4706 or CMDA 2006 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4534 - Applied Statistical Time Series Analysis (3 credits) 
Applied course in time series analysis methods. Topics include regression analysis, detecting and address autocorrelation, modeling seasonal or cyclical trends, creating stationary time series, smoothing techniques, forecasting and forecast errors, and fitting autoregressive integrated moving average models.
Prerequisite(s): STAT 3006 or STAT 4706 or STAT 4714 or STAT 3616 or BIT 2406 or CMDA 2006 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4584 - Advanced Calculus for Statistics (3 credits) 
Introduction to those topics in advanced calculus and linear algebra needed by statistics majors. Infinite sequences and series. Orthogonal matrices, projections, quadratic forms. Extrema of functions of several variables. Multiple integrals, including convolution and nonlinear coordinate changes.
Prerequisite(s): (MATH 1114 or MATH 2114 or MATH 2114H or MATH 2405H) and (MATH 1225) and (MATH 1226) and (MATH 2204 or MATH 2204H or MATH 2406H or CMDA 2005) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4604 - Statistical Methods for Engineers (3 credits) 
Introduction to statistical methodology with emphasis on engineering applications: probability distributions, estimation, hypothesis testing, regression, analysis of variance, quality control. Only one of the courses 4604, 4705, and 4714 may be taken for credit. STAT 4604 partially duplicates STAT 3005, STAT 3615, STAT 3006, STAT 3616 and STAT 4706. Only one may be taken for credit.
Prerequisite(s): MATH 1226 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4654 - Intermediate Data Analytics and Machine Learning (3 credits) 
A technical analytics course. Covers supervised and unsupervised learning strategies, including regression, generalized linear models, regularization, dimension reduction methods, tree-based methods for classification, and clustering. Upper-level analytical methods shown in practice: e.g., advanced naive Bayes and neural networks.
Prerequisite(s): (STAT 3654 or CMDA 3654 or CS 3654) and (CMDA 2006 or STAT 3104 or STAT 4106 or STAT 4706) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: CMDA 4654, CS 4654 
STAT 4664 - Computational Intensive Stochastic Modeling (3 credits) 
Introduction to stochastic modeling methods with an emphasis on computing. Topics include foundational concepts in Monte Carlo simulation (including Monte Carlo integration and tests), identifying sources of stochasticity in models, simulating and analyzing discrete-time and continuous-time stochastic models, visualization and analytical tools for stochastic systems/simulations, and methods for calibrating/fitting stochastic models to data. Real-world applications.
Prerequisite(s): (STAT 4106 or CMDA 3605) and (CS 1114 or CS 1064 or CMDA 2006) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: CMDA 4664 
STAT 4705 - Probability and Statistics for Engineers (3 credits) 
Basic concepts of probability and statistics with emphasis on engineering applications. 4705: Probability, random variables, sampling distributions, estimation, hypothesis testing, simple linear regression correlation, one-way analysis of variance. 4706: Multiple regression, analysis of variance, factorial and fractional experiments. Only one of the courses 3704, 4604, 4705, 4714, and 4724 may be taken for credit.
Prerequisite(s): MATH 2224 or MATH 2204 or MATH 2204H or MATH 2406H or CMDA 2005 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4706 - Probability and Statistics for Engineers (3 credits) 
Basic concepts of probability and statistics with emphasis on engineering applications. 4705: Probability, random variables, sampling distributions, estimation, hypothesis testing, simple linear regression correlation, one-way analysis of variance. 4706: Multiple regression, analysis of variance, factorial and fractional experiments. Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit.
Prerequisite(s): STAT 4705 or STAT 4105 or ISE 2024 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4714 - Probability and Statistics for Electrical Engineers (3 credits) 
Introduction to the concepts of probability, random variables, estimation, hypothesis testing, regression, and analysis of variance with emphasis on application in electrical engineering. Only one of the courses 3704, 4604, 4705, 4714 and 4724 may be taken for credit.
Prerequisite(s): MATH 2224 or MATH 2204 or MATH 2204H or MATH 2406H or CMDA 2005 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4734 - Statistical Methods for Digital Twins (3 credits) 
In-depth understanding of digital twins, focusing on the statistical and analytics methods used to create, validate, and enhance digital twins. Covers applying statistical methods to analyze and quantify the uncertainty of data from digital twins using numerical methods, optimization, machine learning, AI, and various uncertainty quantification techniques. Evaluate different verification, validation, and uncertainty quantification (VVUQ) techniques for digital twins, along with discussing the concepts of generalizability, fairness, and interoperability in digital twins and the role of statistical sciences behind them that make digital twins useful in practice. Applications of digital twins in different industries.
Prerequisite(s): STAT 4214 or CMDA 3654 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4744 - Deep Learning (3 credits) 
Introduction to deep learning, including algorithms, theoretical motivations, and implementation in practice. Basic neural network architectures and optimizations. Multilayer perceptrons, backpropagation, automatic differentiation, and stochastic gradient descent. Convolutional neural networks, recurrent neural networks and the attention mechanism. Generative models, variational autoencoders, and generative adversarial networks. Reinforcement learning, Q learning and design of simple AI systems. Python programming language. Emphasis on efficient implementation, optimization, and scalability. Creation of deep learning models in the context of different types of real applications such as image classification and language processing.
Prerequisite(s): (STAT 3104 or CMDA 2006) and (STAT 4214 or CMDA 4654 or STAT 4654 or CS 4654) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 4804 - Elementary Econometrics (3 credits) 
Economic applications of mathematical and statistical techniques: regression, estimators, hypothesis testing, lagged variables, discrete variables, violations of assumptions, simultaneous equations.
Prerequisite(s): AAEC 1005 and (STAT 3615 or STAT 3005 or STAT 3604 or BIT 2405) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: AAEC 4804 
STAT 4964 - Field Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 4974 - Independent Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 4974H - Independent Study (1-19 credits) 
Honors section.
Instructional Contact Hours: Variable credit course 
STAT 4984 - Special Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 4994 - Undergraduate Research (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 4994H - Undergraduate Research (1-19 credits) 
Honors section.
Instructional Contact Hours: Variable credit course 
STAT 5014 - Introduction to Statistical Program Packages (1 credit) 
Introduction to computing facilities (mainframe and microcomputers), conversational monitoring system (CMS), and statistical program computer packages. Restricted to Statistics majors.
Instructional Contact Hours: (1 Lec, 1 Crd) 
STAT 5024 - Effective Communication in Statistical Consulting (3 credits) 
Communication skills necessary to be effective interdisciplinary statistical collaborators. Explaining and presenting statistical concepts to a non-statistical audience, helping scientists answer their research questions, and managing an effective statistical collaboration meeting.
Prerequisite(s): (STAT 5034 and STAT 5044) or STAT 5615 
Corequisite(s): 5204 or 5616. 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5034 - Inference Fundamentals with Applications to Categorical Data (3 credits) 
Fundamental concepts in statistical inference and related methods: point estimation, interval estimation, hypothesis testing, permutation, and resampling-based methods. Emphasizes use of R programming package, visualizing data, computation and interpretation of effect sizes, statistical simulation to compare the performance of available methods, role of sample size in statistical analysis, contingency tables, and use of model contrasts to assess specific hypotheses in the context of larger models.
Corequisite(s): STAT 5014, STAT 5044 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5044 - Regression and Analysis of Variance (3 credits) 
Principles and methods of data analysis employing linear models for continuous response variables. Topics include both classical descriptive measures and modern computer-based techniques for data visualization; simple, multiple and weighted regression; analysis of variance for one-way and higher-way classifications; fixed, mixed, and random effects models; analysis of covariance; detection and correction of modeling flaws; statistical power.
Prerequisite(s): STAT 5615 and STAT 4584 or MATH 4584 
Corequisite(s): STAT 5014 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5054 - Introduction to Statistical Computing (3 credits) 
Introduction to modern programming packages for data analysis. Basics of coding, language syntax, and statistical functionality to read in raw data files and data sets, subset data, create variables, and recode data. Summaries in the form of tables and graphs. Data analysis using standard statistical methods and data management and analysis of large data sets. Parallel computing. Applied data analysis is emphasized rather than statistical theory. Pre: Graduate standing.
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5104 - Probability and Distribution Theory (3 credits) 
Fundamental concepts of probability, random variables and their distributions, functions of random variables, mathematical expectations, and stochastic convergence.
Prerequisite(s): MATH 4526 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5105G - Advanced Theoretical Statistics (3 credits) 
5105G: Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 5106G: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance. Pre: 5105G: Graduate Standing; 5106G: 5105G.
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5106G - Advanced Theoretical Statistics (3 credits) 
5105G: Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 5106G: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance. Pre: 5105G: Graduate Standing; 5106G: 5105G.
Prerequisite(s): STAT 5105G 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5114 - Statistical Inference (3 credits) 
Decision theoretic formulation of statistical inference, concept and methods of point and confidence set estimation, notion and theory of hypothesis testing, relation between confidence set estimation and hypothesis testing.
Corequisite(s): STAT 5104 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5124 - Linear Models Theory (3 credits) 
A study of the theory underlying the general linear model and general linear hypothesis. Applications in linear regression (full rank) and analysis of variance.
Prerequisite(s): STAT 5114 and MATH 5524 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5134 - Tools and Approaches for Policy-Making in STEM-H Domains (3 credits) 
Techniques for translating theory-driven, qualitative concepts into quantitative data-focused modeling to address policy problems. Quantitative and computational tools including statistical inference and hypothesis testing, system dynamics, and economic analysis. Modeling paradigms and common challenges in modeling. Modern data analytic practices, including good collection, storage and visualization techniques. Problem definitions and application to real-world policy-related problems and implementation in modern software packages. Understanding complexity. Critical evaluation of challenges and common pitfalls in quantitative modeling. Pre: Graduate standing.
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: PSCI 5134, SPIA 5134 
STAT 5154 - Statistical Computing for Data Analytics (3 credits) 
Computational techniques for advanced applied statistical analyses and machine learning methods. Project management for larger data projects including computational constraints, pitfalls, and techniques related to different data types. Advanced report generation across different media, efficient R programming, advanced statistical function writing, parallel statistical computing with R, handling missing data, numerical optimization methods, the EM algorithm, and Monte Carlo methods.
Prerequisite(s): STAT 5054 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5204 - Experimental Design and Analysis I (3 credits) 
Principles and concepts of experimental design; systematic overview and discussion of basic designs from the point of view of blocking, error reduction, and treatment structure; and development of analysis based on linear models.
Prerequisite(s): STAT 5104 or STAT 5616 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5204G - Experimental Design: Concepts and Applications (3 credits) 
Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Completely randomized, randomized complete block and Latin square designs, analysis of covariance, split-plot designs, factorial and fractional factorial designs, incomplete block designs, repeated measures, power and sample size, mean separation procedures.
Prerequisite(s): STAT 5605 or STAT 5615 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5214G - Advanced Methods of Regression Analysis (3 credits) 
Multiple regression including variable selection procedures; detection and effects of multicollinearity; identification and effects of influential observations; residual analysis; use of transformations. Non-linear regression, the use of indicator variables, and logistic regression. Use of SAS.
Prerequisite(s): STAT 5605 or STAT 5615 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5234 - Experimental Design for Data Science (3 credits) 
Understanding data, data collection, and proper data analysis for knowledge discovery and decision-making. Randomization, replication, blocking, data quality evaluations (e.g., representativeness of training data), analysis quality assessment (e.g., robustness of the machine learning algorithm to representativeness of training data). Strengths and weaknesses of experimental designs for data science. Modern qualitative and quantitative techniques for constructing experimental designs and analyzing experimental data. Interpretation and reporting of results.
Prerequisite(s): (STAT 5615 and STAT 5616) or STAT 5525 or CS 5525 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5274 - Advanced Sports Analytics Statistical Research (3 credits) 
Statistical analysis of sports data. Game performance statistics and expected scores. Analysis of player performance, player tracking, team performance, and sports betting. Bayesian methods and prediction models applied to sports data. Decision-making. Assessing sports analytics research and literature. Pre: Graduate Standing.
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5364G - Advanced Statistical Genomics (3 credits) 
Statistical methods for bioinformatics and genetic studies, with an emphasis on statistical analysis, assumptions and problem-solving. Topics include: basic concepts of genes and genomes, commonly used statistical methods for gene identification, association mapping and other related problems. Focus on statistical tools for gene expression studies and association studies, multiple comparison procedures, likelihood inference and preparation for advanced study in the areas of bioinformatics and statistical genetics.
Prerequisite(s): STAT 5616 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5374 - Statistical Epidemiology and Observation Studies (3 credits) 
Statistical methodology for epidemiology and observational studies. Statistical evaluation and inference for risk and prevalence of population safety and disease risk factors. Epidemiology and observational study design. Emphasis on casual inference and statistical models. Pre: 5034 or 5124 or 5615.
Prerequisite(s): STAT 5034 or STAT 5124 or STAT 5615 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5414 - Time Series Analysis I (3 credits) 
Analysis of data when observations are not mutually independent, stationary and nonstationary time series, linear filtering, trend elimination, prediction, and applications in economics and engineering. Even years.
Prerequisite(s): STAT 5114 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5434 - Applied Stochastic Processes (3 credits) 
Stochastic processes in statistical applications including Markov chains, Poisson processes, renewal processes, branching processes, random walks, martingales, Brownian motion and related stationary Gaussian processes.
Prerequisite(s): STAT 5104 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5444 - Bayesian Statistics (3 credits) 
Introductory course of Bayesian statistics on basic concepts of probability, Bayesian inference of Normal, Binomial, Poisson, Uniform and other common distributions, selections of prior information, Bayesian decision theory, Bayesian analysis of regression and analysis of variance and Bayesian foundation. Even years.
Prerequisite(s): STAT 5114 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5444G - Advanced Applied Bayesian Statistics (3 credits) 
Bayesian methodology with emphasis on applied statistical problems: data displaying, prior distribution elicitation, posterior analysis, models for proportions, means and regression. Pre: Graduate Standing.
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5454 - Reliability Theory (3 credits) 
Basic concepts of lifetime distributions, types of censoring, inference procedures for exponential, Weibull and extreme value distributions, nonparametric estimation of survival function, kernel density estimation, accelerated life testing, and goodness of fit tests.
Prerequisite(s): STAT 4106 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5474 - Statistical Theory of Quality Control (3 credits) 
Development of statistical concepts and theory underlying procedures used in quality control applications. Sampling inspection procedures, the sequential probability ratio test, continuous sampling procedures, process control procedures, and experimental design.
Prerequisite(s): STAT 5104 and STAT 5114 
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: ISE 5474 
STAT 5484 - Applied Economic Forecasting (3 credits) 
Forecasting economic, agricultural and environmental data using basic linear and non-linear time series models. Emphasis on programming and computational implementation of time series model-selection techniques and practical applications. Pre: Graduate Standing.
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: AAEC 5484 
STAT 5504 - Multivariate Statistical Methods (3 credits) 
Methods of inference for multivariate distributions. Multivariate distributions, location and dispersion problems for one and two samples, multivariate analysis of variance, linear models, repeated measurements, inference for dispersion and association parameters, principal components, discriminant and cluster analysis, and simultaneous inference. R will be used.
Prerequisite(s): STAT 5104 or STAT 5616 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5504G - Advanced Applied Multivariate Analysis (3 credits) 
Non-mathematical study of multivariate analysis. Multivariate analogs of uinivariate test and estimation procedures. Simultaneous inference procedures. Multivariate analysis of variance, repeated measures, inference for dispersion and association parameters, principle components analysis, discriminant analysis, cluster analysis. Prerequisite: Graduate Standing required
Prerequisite(s): STAT 5616 or STAT 5606 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5514 - Regression Analysis (3 credits) 
Classical and modern techniques in regression analysis. Use of modern regression techniques to diagnose collinearity, leverage, and outliers. Model discrimination using cross validation techniques. The study of transformations, biased estimation, and nonlinear regression.
Prerequisite(s): STAT 5124 or STAT 5616 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5514G - Advanced Introduction to Categorial Data Analysis (3 credits) 
Statistical approaches to analyze categorical data. Probability computation and distribution specification, interval estimation and hypothesis testing, formulating and fitting generalized linear models including logistic and Poisson regression, algorithms used for model fitting, variable selection, and classification trees and supervised learning. Pre: Graduate Standing.
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5525 - Statistical Learning (3 credits) 
Theory and application of supervised and unsupervised methods of statistical and machine learning. 5525: Methods of supervised statistical and machine learning for regression and classification. Overview of statistical (data) and algorithmic models. Detailed study of regression models for continuous and discrete data (linear, nonlinear, and generalized linear models). Detailed study of methods for classifying categorical outcomes (logistic and multinomial models, discriminant analysis, naïve Bayes). Tree-based methods for regression and classification. Feature selection, regularization, and dimension reduction for high-dimensional problems (Lasso, Ridge, PCR, PLS). Cross-validation and resampling for model tuning and uncertainty estimation. Statistical analyses using R or Python. 5526: Supervised and unsupervised statistical and machine learning for complex or high-dimensional data. Methods include: global and local models with smoothing (nearest-neighbor, kernel, and basis expansion techniques). Generalized (linear and additive) models. Methods for correlated (clustered) data, mixed models. Unsupervised learning for summarization, visualization, dimension reduction, imputation, and grouping; (K-means, PCA, hierarchical clustering, model-based clustering, association rules, self-organizing maps, and biclustering). Ensemble learning (bagging, model averaging, boosting, stacking). Support vector machines and neural networks. Introduction to methods and algorithms for deep learning. Model interpretability and explainability. Statistical analyses using R or Python. Pre: Graduate Standing.
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: ADS 5525 
STAT 5526 - Statistical Learning (3 credits) 
Theory and application of supervised and unsupervised methods of statistical and machine learning. 5525: Methods of supervised statistical and machine learning for regression and classification. Overview of statistical (data) and algorithmic models. Detailed study of regression models for continuous and discrete data (linear, nonlinear, and generalized linear models). Detailed study of methods for classifying categorical outcomes (logistic and multinomial models, discriminant analysis, naïve Bayes). Tree-based methods for regression and classification. Feature selection, regularization, and dimension reduction for high-dimensional problems (Lasso, Ridge, PCR, PLS). Cross-validation and resampling for model tuning and uncertainty estimation. Statistical analyses using R or Python. 5526: Supervised and unsupervised statistical and machine learning for complex or high-dimensional data. Methods include: global and local models with smoothing (nearest-neighbor, kernel, and basis expansion techniques). Generalized (linear and additive) models. Methods for correlated (clustered) data, mixed models. Unsupervised learning for summarization, visualization, dimension reduction, imputation, and grouping; (K-means, PCA, hierarchical clustering, model-based clustering, association rules, self-organizing maps, and biclustering). Ensemble learning (bagging, model averaging, boosting, stacking). Support vector machines and neural networks for classification and regression. Introduction to methods and algorithms for deep learning. Model interpretability and explainability. Statistical analyses using R or Python.
Prerequisite(s): STAT 5525 or ADS 5525 
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: ADS 5526 
STAT 5544 - Spatial Statistics (3 credits) 
Spatial data structures: geostatistical data, lattices and point patterns. Stationary and isotropic random fields. Autocorrelated data structures. Semivariogram estimation and spatial prediction for geostatistical data. Mapped and sampled point patterns. Regular, completely random and clustered point processes. Spatial regression and neighborhood analyses for data on lattices.
Prerequisite(s): STAT 5124 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5554 - Functional Data Analysis (3 credits) 
Functional summary statistics, phase-plane plots, functional principal component analysis, functional regression models, principal differential analysis, dynamic models, analysis of manifold data, topological data analysis, data analysis of complex objects.
Prerequisite(s): STAT 5124 and STAT 5114 and STAT 5044 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5574 - Response Surface Design and Analysis I (3 credits) 
Use of response surface analysis to design and analyze industrial experiments. First and second order models. First and second order experimental designs. Use of model diagnostics for finding optimum operating conditions. Even years.
Prerequisite(s): STAT 5204 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5605 - Biometry (3 credits) 
An applied introduction to statistical reasoning in the health and behavioral sciences, emphasizing interpretation, communication, transparency, and reproducible data analysis workflow. STAT 5605 topics include: data types, data cleaning, study designs, exploratory data analysis (descriptive statistics and data visualization), the central limit theorem, sampling variability, statistical inference (confidence interval and hypothesis testing), and regression techniques (one-way Analysis of Variance (ANOVA), simple linear regression, multiple regression, logistic regression), with an emphasis on rigor and reproducibility. STAT 5606 topics include: model building and selection, tree-based methods, generalized linear models, mixed effect models, survival analysis, clinical trials/study designs, missing data, power and sample size determination, and Statistical Analysis Plans (SAP) and Data Management Plans (DMP). Uses R/RStudio.
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5606 - Biometry (3 credits) 
An applied introduction to statistical reasoning in the health and behavioral sciences, emphasizing interpretation, communication, transparency, and reproducible data analysis workflow. STAT 5605 topics include: data types, data cleaning, study designs, exploratory data analysis (descriptive statistics and data visualization), the central limit theorem, sampling variability, statistical inference (confidence interval and hypothesis testing), and regression techniques (one-way Analysis of Variance (ANOVA), simple linear regression, multiple regression, logistic regression), with an emphasis on rigor and reproducibility. STAT 5606 topics include: model building and selection, tree-based methods, generalized linear models, mixed effect models, survival analysis, clinical trials/study designs, missing data, power and sample size determination, and Statistical Analysis Plans (SAP) and Data Management Plans (DMP). Uses R/RStudio.
Prerequisite(s): STAT 5605 or STAT 5615 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5615 - Statistics in Research (3 credits) 
5615: Concepts in statistical inference, including basic probability, estimation, and test of hypothesis, point and interval estimation and inferences; categorical data analysis; simple linear regression; and one-way analysis of variance. 5616: Multiple linear regression; multi-way classification analysis of variance; randomized block designs; nested designs; and analysis of covariance. One year of Calculus. CMS.
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5616 - Statistics in Research (3 credits) 
5615: Concepts in statistical inference, including basic probability, estimation, and test of hypothesis, point and interval estimation and inferences; categorical data analysis; simple linear regression; and one-way analysis of variance. 5616: Multiple linear regression; multi-way classification analysis of variance; randomized block designs; nested designs; and analysis of covariance. One year of Calculus and knowledge of CMS required.
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5664 - Applied Statistical Time Series Analysis for Reseach Scientists (3 credits) 
Applied course in time series analysis methods. Topics include regression analysis, detecting and addressing autocorrelation, modeling seasonal or cyclical trends, creating stationary time series, smoothing techniques, forecasting errors, and fitting autoregressive integrated moving average models.
Prerequisite(s): STAT 5616 or STAT 5606 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5684 - Survival Analysis (3 credits) 
Models and methods for time-to-event data with focus on biological and biomedical applications. Topics includes types of censoring and truncation; likelihood construction; survival function estimation; nonparametric two or more samples tests; Cox semiparametric regression, time-dependent covariates; regression diagnostics; competing risks; frailty model. Pre-requisite: Working knowledge of statistical software.
Prerequisite(s): STAT 5044 and STAT 5104 and STAT 5114 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5744 - Advanced Deep Learning (3 credits) 
Introduction to deep learning, including algorithms, theoretical motivations, and implementation in practice. Basic neural network architectures and optimizations. Multilayer perceptrons, backpropagation, automatic differentiation, and stochastic gradient descent. Convolutional neural networks, recurrent neural networks and the attention mechanism. Generative models, variational autoencoders, and generative adversarial networks. Reinforcement learning, Q learning and design of simple AI systems. Python programming language. Emphasis on efficient implementation, optimization, and scalability. Creation of deep learning models in the context of real applications, such as image classification and language processing. Pre: Graduate Standing. Knowledge of statistical theory, optimization theory, and programming proficiency (particularly in Python and associated scientific computing libraries)
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5754 - Internship in Statistics (1-6 credits) 
Full time, supervised internship experience at a company or government agency performing statistical analysis. May be repeated for a maximum of 3 hours toward an M.S. degree and 6 hours toward a Ph.D. degree. Graduate standing in statistics and permission of department required.
Prerequisite(s): STAT 5024 
Instructional Contact Hours: (1-6 Lec, 1-6 Crd) 
Repeatability: up to 6 credit hours 
STAT 5894 - Final Examination (3 credits) 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 5904 - Project and Report (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 5924 - Graduate Seminar (1 credit) 
Special topics in statistical theory and applications. May be taken for credit two times (max. 2C).
Instructional Contact Hours: (1 Lec, 1 Crd) 
STAT 5974 - Independent Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 5984 - Special Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 5994 - Research and Thesis (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 6105 - Measure and Probability (3 credits) 
Development of measure theoretic foundations of probability theory. 6105: sigma fields, probability, and general measures; random variables, measurability and distributions, integration, and expectation; product measures; Radon-Nikodym theorem and conditioning. 6106: Random variables and strong and weak laws of large numbers; characteristic functions, central limit theorem and martingales; stochastic processes and Brownian motion. 6105 partially duplicates Math 5225. Must be enrolled in PhD program.
Prerequisite(s): STAT 5104 or MATH 4525 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 6114 - Advanced Topics in Statistical Inference (3 credits) 
Advanced course in the theory of inference for graduate students in statistics and other qualified graduate students. Develops foundations, sufficiency, information, estimation, hypothesis testing, invariance, and unbiasedness.
Prerequisite(s): STAT 5114 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 6344 - Modeling for High Dimensional and Sparse Data (3 credits) 
Statistical methods and modern computational methods for analyzing high dimensional data and sparse data, methods applied to complex data structures in various fields (e.g., genomics, epidemiology, and data mining), screening tools and matrix approximation, modeling strategies for high dimensional sparse data (parametric, nonparametric, and semiparametric regression models), statistical inference, graphical modeling methods, signal approximation methods, method limitations, functional analysis, causal inference, and data integration.
Prerequisite(s): STAT 5114 and STAT 5514 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 6474 - Adv Topics Bayesian Statistics (3 credits) 
Advanced concepts and methods in Bayesian analysis, including specifying priors, large sample theory, adaptive rejection sampling, adaptive rejection metropolis Hastings sampling, reverse jump Markov Chain Monte Carlo, model selection, nonparametric and semiparametric Bayesian methods using nonparametric priors, and Bayesian survival models.
Prerequisite(s): STAT 5114 and STAT 5514 and STAT 5444 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 6504 - Experimental Design and Analysis II (3 credits) 
Theoretical treatment of construction and analysis of various types of incomplete block and factorial designs.
Prerequisite(s): STAT 5124 and STAT 5204 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 6514 - Advanced Topics in Regression (3 credits) 
Advanced notions in modern regression techniques and diagnostics. The underlying theory and concepts associated with estimation methods for handling collinearity. Theory behind modern criteria for selection of candidate models. The development of single and multiple outlier and influence diagnostics. Odd years.
Prerequisite(s): STAT 5124 and STAT 5514 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 6544 - Surrogate Modeling (3 credits) 
Statistical techniques at the interface between mathematical modeling via computer simulation, computer model meta-modeling (i.e., emulation/surrogate modeling), calibration to field data, and geometric and model-based sequential design, and Bayesian optimization. Historical literature, canoncial examples, and modern nonparametric methods like Gaussian processes. Computation and implementation, fidelity enhancements and approximate methods for big data. Real-world field experiments and computer model simulations from the physical and engineering sciences.
Prerequisite(s): STAT 5044 and STAT 5204 and STAT 5304 and STAT 5444 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 6554 - Advanced Statistical Computing (3 credits) 
A second course on statistical and scientific computing. Hands-on, statistical implementation leveraging modern desktop computing (multiple cores), cluster computing (multiple nodes) and distributed computing (hadoop/Amazon EC2) and the coming wave of exascale computing (GPU/TPU/Xeon Phi). Fundamentals of the Unix shell, manipulating data therein, compiling libraries with make, version control (e.g., Git), good habits/best practice with code development and data management. Using advanced R skills to design statistical applications and bind together other languages (e.g., C, C++, Fortran, awk, sed, Cuda, etc.), databases, computing architectures and interfaces to address statistical problems.
Prerequisite(s): STAT 5054 
Instructional Contact Hours: (3 Lec, 3 Crd) 
STAT 6564 - Bayesian Econometric Analysis (3 credits) 
Bayesian estimation of economic models, with focus on Gibbs sampling, hierarchical modeling, data augmentation, and model search. Strong emphasis on programming and computational implementation.
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: AAEC 6564, ECON 6564 
STAT 6634 - Advanced Statistics for Education (3 credits) 
Multiple regression procedures for analyzing data as applied in educational settings, including curvilinear regressions, dummy variables, multicollinearity, and introduction to path analysis.
Prerequisite(s): STAT 5634 
Instructional Contact Hours: (3 Lec, 3 Crd) 
Course Crosslist: EDRE 6634 
STAT 6984 - Special Study (1-19 credits) 
Instructional Contact Hours: Variable credit course 
STAT 7994 - Research and Dissertation (1-19 credits) 
Instructional Contact Hours: Variable credit course