## Undergraduate Course List

Course Number |
Course Name |
Description |

STAT 1000Q | Introduction to Statistics I | A standard approach to statistical analysis primarily for students of business and economics; elementary probability, sampling distributions, normal theory estimation and hypothesis testing, regression and correlation, exploratory data analysis. Learning to do statistical analysis on a personal computer is an integral part of the course. |

STAT 1100Q | Elementary Concepts of Statistics | Standard and nonparametric approaches to statistical analysis; exploratory data analysis, elementary probability, sampling distributions, estimation and hypothesis testing, one- and two-sample procedures, regression and correlation. Learning to do statistical analysis on a personal computer is an integral part of the course. |

STAT 2215Q | Introduction to Statistics II | Analysis of variance, multiple regression, chi-square tests, and non-parametric procedures. |

STAT 3005 | Biostatistics for Health Professions | Introduction to biostatistical techniques, concepts, and reasoning using a broad range of biomedical and public health related scenarios. Specific topics include description of data, statistical hypothesis testing and its application to group comparisons, and tools for modeling different type of data, including categorical, and time-event, data. Emphasis on the distinction of these methods, their implementation using statistical software, and the interpretation of results applied to health sciences research questions and variables. |

STAT 3025Q | Statistical Methods (Calculus Level I) | Basic probability distributions, point and interval estimation, tests of hypotheses, correlation and regression, analysis of variance, experimental design, non-parametric procedures. |

STAT 3115Q | Analysis of Experiments | Straight-line regression, multiple regression, regression diagnostics, transformations, dummy variables, one-way and two-way analysis of variance, analysis of covariance, stepwise regression. |

STAT 3345Q | Probability Models for Engineers | Probability set functions, random variables, expectations, moment generating functions, discrete and continuous random variables, joint and conditional distributions, multinomial distribution, bivariate normal distribution, functions of random variables, central limit theorems, computer simulation of probability models. |

STAT 3375Q | Introduction to Mathematical Statistics | The mathematical theory underlying statistical methods. Probability spaces, distributions in one and several dimensions, generating functions, limit theorems, sampling, parameter estimation. Neyman-Pearson theory of hypothesis testing, correlation, regression, analysis of variance. |

STAT 3445 | Introduction to Mathematical Statistics | The mathematical theory underlying statistical methods. Probability spaces, distributions in one and several dimensions, generating functions, limit theorems, sampling, parameter estimation. Neyman-Pearson theory of hypothesis testing, correlation, regression, analysis of variance. |

STAT 3494W | Undergraduate Seminar II | The student will attend 6-8 seminars per semester, and choose one statistical topic to investigate in detail. The student will write a well revised comprehensive paper on this topic, including a literature review, description of technical details, and a summary and discussion. |

STAT 3515Q | Design of Experiments | Methods of designing experiments utilizing regression analysis and the analysis of variance. |

STAT 3675Q | Statistical Computing | Introduction to computing for statistical problems; obtaining features of distributions, fitting models and implementing inference (obtaining confidence intervals and running hypothesis tests); simulation-based approaches and basic numerical methods. One hour per week devoted to computing and programming skills. |

STAT 3965 | Elementary Stochastic Processes | Conditional distributions, discrete and continuous time Markov chains, limit theorems for Markov chains, random walks, Poisson processes, compound and marked Poisson processes, and Brownian motion. Selected applications from actuarial science, biology, engineering, or finance. |

STAT 4185 | Special Topics in Data Science | The purpose of this course is to gain understanding on the basic and critical importance of data science with applications to clinical drug development. Successful completion of an introductory course in statistics is a prerequisite for this course. |

STAT 4188 | Generalized Linear Models | Statistical models for the analysis of quantitative and qualitative data, of the types usually encountered in social science, public health, biological and life sciences research. |

STAT 4190 | Field Study Internship | Supervised field work relevant to some area of Statistics with a regional industry, government agency, or non-profit organization. Evaluated by the field supervisor and by the instructor (based on a detailed written report submitted by the student). |

STAT 4255 | Introduction to Statistical Learning | Modern statistical learning methods arising frequently in data science and machine learning with real-world applications: linear and logistic regression, generalized additive models, decision trees, boosting, support vector machines, and neural networks (deep learning). Prerequisites: STAT 3115Q or instructor consent |

STAT 4299 | Independent Study | Credits and hours by arrangement. Prerequisite: Open only with consent of instructor. May be repeated for credit. |

STAT 4389 | Undergraduate Research | Supervised research in probability or statistics. A final written report and oral presentation are required. |

STAT 4475 | Statistical Quality Control and Reliability | Development of control charts, acceptance sampling and process capability indices, reliability modeling, regression models for reliability data, and proportional hazards models for survival data. |

STAT 4525 | Sampling Theory | Sampling and nonsampling error, bias, sampling design, simple random sampling, sampling with unequal probabilities, stratified sampling, optimum allocation, proportional allocation, ratio estimators, regression estimators, super population approaches, inferences in finite populations. |

STAT 4535 | Introduction to Operations Research | Introduction to the use of mathematical and statistical techniques to solve a wide variety of organizational problems. Topics include linear programming, network analysis, queueing theory, decision analysis. |

STAT 4625 | Introduction to Biostatistics | Rates and proportions, sensitivity, specificity, two-way tables, odd ratios, relative risk, ordered and non-ordered classifications, trends, case-control studies, elements of regression including logistic and Poisson, additivity and interaction, combination of studies and meta-analysis. |

STAT 4675 | Probability and Statistics Problems | Designed to help students prepare for the second actuarial examination. |

STAT 4825 | Applied Time Series | Introduction to prediction using time-series regression methods with non-seasonal and seasonal data. Smoothing methods for forecasting. Modeling and forecasting using univariate, autoregressive, moving average models. |

STAT 4875 | Nonparametric Methods | Basic ideas, the empirical distribution function and its applications, uses of order statistics, one- two- and c-sample problems, rank correlation, efficiency. |

## Graduate Course List

Course Number |
Course Name |
Description |

STAT 5005 | Introduction to Applied Statistics | One-, two- and k-sample problems, regression, elementary factorial and repeated measures designs, covariance. Use of computer packages, e.g., SAS and MINITAB. Prerequisite: Not open to students who have passed STAT 201 or STAT 2215Q (RG613). |

STAT 5099 / BIST 5099 | Investigation of Special Topics | Topical seminar course. |

STAT 5105 | Quantitative Methods in the Behavioral Sciences | A course designed to acquaint the student with the application of statistical methods in the behavioral sciences. Correlational methods include multiple regression and related multivariate techniques. |

STAT 5125/ BIST 5125 | Computing for Statistical Data Science | Principles and practice of statistical computing in data science: data structure, data programming, data visualization, simulation, resampling methods, distributed computing, and project management tools. Prerequisites: Introductory course in mathematical and applied statistics; introductory course in programming. Instructor consent required. |

STAT 5192 | Supervised Research in Statistics | Supervised Research |

STAT 5215 / BIST 5215 | Statistical Consulting | Applied inference for academia, government, and industry: ethical guidelines, observational studies, surveys, clinical trials, designed experiments, data management, aspects of verbal and written communication, case studies. Prerequisites: STAT 5315, STAT 5505, STAT 5605 and STAT 5725, or instructor consent. |

STAT 5225 / BIST 5225 | Data Management and Programming in R and SAS | Creation and management of datasets for statistical analysis: software tools and databases, user-defined functions, importing/exporting/manipulation of data, conditional and iterative processing, generation of reports. Prerequisites: STAT 5505 and 5605 or instructor consent. |

STAT 5315 | Analysis of Experiments | Straight-line regression, multiple regression, regression diagnostics, transformations, dummy variables, one-way and two-way analysis of variance, analysis of covariance, stepwise regression. Prerequisite: STAT 5005. Not open to students who have passed STAT 242 or STAT 3115Q (RG614). |

STAT 5361 / BIST 5361 | Statistical Computing | Use of computing for statistical problems; obtaining features of distributions, fitting models and implementing inference. Basic numerical methods, nonlinear statistical methods, numerical integration, modern simulation methods. Open to graduate students in Statistics, others with permission (RG814). |

STAT 5405 | Applied Statistics for Data Science | Statistics essential for data science incorporating descriptive statistics; integrative numerical description and visualization of data; graphical methods for determining and comparing distributions of data; data-driven statistical inference of one-sample, two-sample, and k-sample problems; linear and non-linear regression. Prerequisites: Introductory course in mathematical statistics and regression analysis or instructor consent. |

STAT 5415 | Mathematical Statistics for Data Science | Discrete and continuous random variables, exponential family, joint and conditional distributions, order statistics, statistical inference:point estimation, confidence interval estimation, and hypothesis testing. Open to graduate students in Statistics, others with permission (RG814). |

STAT 5505 / BIST 5505 | Applied Statistics I | Exploratory data analysis: stem-and leaf plots, Box-plots, symmetry plots, quantile plots, transformations, discrete and continuous distributions, goodness of fit tests, parametric and non-parametric inference for one sample and two sample problems, robust estimation, Monte Carlo inference, bootstrapping. Open to graduate students in Statistics, others with permission (RG814). |

STAT 5515 / BIST 5515 | Design of Experiments | One way analysis of variance, multiple comparison of means, randomized block designs, Latin and Graeco-Latin square designs, factorial designs, two-level factorial and fractional factorial designs, nested and hierarchical designs, split-plot designs. Prerequisite: STAT 5005. Not open to students who have passed STAT 243 or STAT 3515Q (RG615). |

STAT 5525 / BIST 5525 | Sampling Theory | Sampling and nonsampling error, bias, sampling design, simple random sampling, sampling with unequal probabilities, stratified sampling, optimum allocation, proportional allocation, ratio estimators, regression estimators, super population approaches, inference in finite populations. Open to graduate students in Statistics, others with permission (RG814). |

STAT 5535 / BIST 5535 | Nonparametric Methods | Theory and applications of statistical methods for analyzing ordinal, non-normal data: one and multiple sample hypothesis testing, empirical distribution functions and applications, order statistics, rank tests, efficiency, linear and nonlinear regression, classification. Not open to students who have passed STAT 4875. |

STAT 5585 / BIST 5585 | Mathematical Statistics I | Introduction to probability theory, transformations and expectations, moment generating function, discrete and continuous distributions, joint and marginal distributions of random vectors, conditional distributions and independence, sums of random variables, order statistics, convergence of a sequence of random variables, the central limit theorem. |

STAT 5605 / BIST 5605 | Applied Statistics II | Analysis of variance, regression and correlation, analysis of covariance, general linear models, robust regression procedures, and regression diagnostics. Prerequisite: STAT 5505 (RG815). |

STAT 5615 / BIST 5615 | Categorical Data Analysis | Statistical analysis of data on a nominal scale: discrete distributions, contingency tables, odds ratios, interval estimates, goodness of fit tests, logistic/probit/complementary log-log regression, Poisson-related regression. Prerequisites: STAT 5505 and 5605, or instructor consent. |

STAT 5625 / BIST 5625 | Introduction to Biostatistics | Rates and proportions, sensitivity, specificity, two-way tables, odds ratios, relative risk, ordered and non-ordered classifications, rends, case-control studies, elements of regression including logistic and Poisson, additivity and interaction, combination of studies and meta-analysis. |

STAT 5635 / BIST 5635 | Clinical Trials | Basic concepts of clinical trial analysis; controls, randomization, blinding, surrogate endpoints, sample size calculations, sequential monitoring, side-effect evaluation and intention-to-treat analyses. Also, experimental designs including dose response study, multicenter trials, clinical trials for drug development, stratification, and cross-over trials. |

STAT 5645 / BIST 5645 | Concepts and Analysis of Survival Data | Survival models, censoring and truncation, nonparametric estimation of survival functions, comparison of treatment groups, mathematical and graphical methods for assessing goodness of fit, parametric and nonparametric regression models. |

STAT 5655 / BIST 5655 | Epidemiology | The statistical study of health and illness in human and veterinary populations: epidemiological study designs, measures of disease frequency/effect/potential impact, selection and information biases, confounding, stratified analysis. Prerequisites: Open to graduate students in the Department of Statistics, others with consent. |

STAT 5665 / BIST 5665 | Applied Multivariate Analysis | Multivariate normal distributions, inference about a mean vector, comparison of several multivariate means, principal components, factor analysis, canonical correlation analysis, discrimination and classification, cluster analysis. Open to graduate students in Statistics, others with permission (RG814). |

STAT 5675 / BIST 5675 | Bayesian Data Analysis | Theory of statistical inference based on Bayes’ Theorem: basic probability theory, linear/nonlinear, graphical, and hierarchical models, decision theory, Bayes estimation and hypothesis testing, prior elicitation, Gibbs sampling, the Metropolis-Hastings algorithm, Monte Carlo integration. Prerequisites: STAT 5585 and STAT 5685, or instructor consent. |

STAT 5685 / BIST 5685 | Mathematical Statistics II | The sufficiency principle, the likelihood principle, the invariance principle, point estimation, methods of evaluating point estimators, hypotheses testing, methods of evaluating tests, interval estimation, methods of evaluating interval estimators. Prerequisite: STAT 5585 (RG816). |

STAT 5705 / BIST 5705 | Statistical Methods in Bioinformatics | Statistical methods and software tools for the analysis of biological data: sequencing methods; gene alignment methods; expression analysis; evolutionary models; analysis of proteomics, metabolomics, and methylation data; pathway analysis: gene network analysis. Prerequisites: STAT 5505 and STAT 5585, or instructor consent. |

STAT 5725 / BIST 5725 | Linear Models I | Linear and matrix algebra concepts, generalized inverses of matrices, multivariate normal distribution, distributions of quadratic forms in normal random vectors, least squares estimation for full rank and less than full rank linear models, estimation under linear restrictions, testing linear hypotheses. Prerequisites: Open to graduate students in Statistics, others with permission (RG814). |

STAT 5735 / BIST 5735 | Linear Models II | Multiple comparisons, fixed-effects linear models, random-effects and mixed-effects models, generalized linear models, variable selections, regularization and sparsity, support vector machines, additive models, and Bayesian linear models. Prerequisites: STAT/BIST 5725, STAT/BIST 5505, and STAT/BIST 5605. Open to students who have passed the PhD Qualifying Examination in Statistics; others with permission. |

STAT 5815 / BIST 5815 | Longitudinal Data Analysis |
Statistical theory and methodology for data collected over time in a clustered manner: design of experiments, exploratory data analysis, linear models for continuous data, general linear models for discrete data, marginal and mixed models, treatment of missing data. Prerequisites: STAT 5505 and STAT 5605, or instructor consent. |

STAT 5825 / BIST 5825 | Applied Time Series | Introduction to prediction using time-series regression methods with non-seasonal and seasonal data. Smoothing methods for forecasting. Modeling and forecasting using univariate autoregressive moving average models. Open to graduate students in Statistics, others with permission (RG814). |

STAT 5915/ BIST 5915 | Statistical Data Science in Action | Real-world statistical data science practice: problem formulation; integration of statistics, computing, and domain knowledge; collaboration; communication; reproducibility; project management. Prerequisites: STAT 5405 or instructor consent. |

STAT 6315 | Statistical Inference I | Exponential families, sufficient statistics, loss function, decision rules, convexity, prior information, unbiasedness, Bayesian analysis, minimaxity, admissibility, simultaneous and shrinkage estimation, invariance, equivariant estimation. Open to graduate students in Statistics, others with permission (RG814). |

STAT 6325 | Advanced Probability | Fundamentals of measure and integration theory: fields, o-fields, and measures; extension of measures; Lebesgue-Stieltjes measures and distribution functions; measurable functions and integration theorems; the Radon-Nikodym Theorem, product measures, and Fubini’s Theorem. Introduction to measure-theoretic probability: probability spaces and random variables; expectation and moments; independence, conditioning, the Borel-Cantelli Lemmas, and other topics as time allows. Open to graduate students in Statistics, others with permission (RG814). |

STAT 6494 / BIST 6494 | Seminar in Applied Statistics | Open to graduate students in Statistics, others with permission (RG814). |

STAT 6515 | Statistical Inference II | Statistics and subfields, conditional expectations and probability distributions, uniformly most powerful tests, uniformly most powerful unbiased tests, confidence sets, conditional inference, robustness, change point problems, order restricted inference, asymptotics of likelihood ratio tests. Open to graduate students in Statisitcs, others with permission. Prerequisite: STAT 6315 (RG527). |

STAT 6615 | Statistical Learning and Optimization | Computationally intensive statistical learning methods with optimization techniques: classification, discriminant analysis, (generalized) additive models, boosting, regression trees, regularized regression, principal components, support vector machines, and (deep) neural networks. Development of intermediate expressive and receptive skills in ASL. Prerequisites: Intermediate courses in mathematical and applied statistics. Instructor consent required. |

STAT 6694 | Seminar in Multivariate Statistics | Open to graduate students in Statistics, others with permission (RG814). |

STAT 6894 | Seminar in the Theory of Probability and Stochastic Processes | Open to graduate students in Statistics, others with permission (RG814). |