The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.

### 5005. Introduction to Applied Statistics

3.00 credits

Prerequisites: Not open to students who have passed STAT 2215Q.

Grading Basis: Graded

One-, two- and k-sample problems, regression, elementary factorial and repeated measures designs, covariance. Use of computer packages, e.g., SAS and MINITAB. STAT 5005 cannot be counted toward a graduate degree in Statistics or Biostatistics.

View Classes »### 5095. Investigation of Special Topics

1.00 - 3.00 credits | May be repeated for a total of 3 credits.

Prerequisites: None.

Grading Basis: Graded

Topical seminar course. May be repeated for a maximum of three credits with a change of topic.

View Classes »### 5105. Quantitative Methods in the Behavioral Sciences

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

AcquaintS the student with the application of statistical methods in the behavioral sciences. Correlational methods include multiple regression and related multivariate techniques.

View Classes »### 5125. Computing for Statistical Data Science

3.00 credits

Prerequisites: Instructor consent and introductory course in mathematical and applied statistics; introductory course in programming.

Grading Basis: Graded

Principles and practice of statistical computing in data science: data structure, data programming, data visualization, simulation, resampling methods, distributed computing, and project management tools.

View Classes »### 5192. Supervised Research in Statistics

1.00 - 6.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

### 5215. Statistical Consulting

Also offered as: BIST 5215

3.00 credits

Prerequisites: BIST/STAT 5315, 5505, and 5605; or instructor consent.

Grading Basis: Graded

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.

View Classes »### 5225. Data Management and Programming in R and SAS

Also offered as: BIST 5225

3.00 credits

Prerequisites: BIST/STAT 5505 and 5605; or instructor consent.

Grading Basis: Graded

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.

View Classes »### 5255. Introduction to Data Science

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission. Not open for credit to students who have passed STAT 3255. Recommended preparation: STAT 1000Q or 1100Q or 5005 or equivalent; STAT 2255 or equivalent; and STAT 3115Q or equivalent.

Grading Basis: Graded

Introduction to data science for effectively storing, processing, visualizing, analyzing and making inferences from data to enable decision making. Topics include project management, data preparation, data visualization, statistical modeling, machine learning, distributed computing and ethics.

View Classes »### 5315. Analysis of Experiments

3.00 credits

Prerequisites: STAT 5005. Not open to students who have passed STAT 3115Q.

Grading Basis: Graded

Graded Straight-line regression, multiple regression, regression diagnostics, transformations, dummy variables, one-way and two-way analysis of variance, analysis of covariance, stepwise regression. STAT 5315 cannot be counted toward a graduate degree in Statistics or Biostatistics.

View Classes »### 5361. Statistical Computing

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

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.

View Classes »### 5405. Applied Statistics for Data Science

3.00 credits

Prerequisites: Instructor consent and introductory course in mathematical statistics and regression analysis. Not open to students who have passed STAT 5505 or STAT 5605 or BIST 5505 or BIST 5605.

Grading Basis: Graded

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 regression; and non-linear regression.

View Classes »### 5415. Mathematical Statistics for Data Science

3.00 credits

Prerequisites: Open only to Statistics graduate students; instructor consent required. Recommended preparation: Basic statistics. Not open to students who have passed STAT 5585 or STAT 5685 or BIST 5585 or BIST 5685.

Grading Basis: Graded

Discrete and continuous random variables, exponential family, joint and conditional distributions, order statistics, statistical inference: point estimation, confidence interval estimation, and hypothesis testing.

View Classes »### 5505. Applied Statistics I

Also offered as: BIST 5505

3.00 credits

Prerequisites: Open to graduate students in Statistics and Biostatistics; others with permission.

Grading Basis: Graded

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.

View Classes »### 5515. Design of Experiments

Also offered as: BIST 5515

3.00 credits

Prerequisites: STAT 5005 or statistics MA or PHD field of study. Not open to students who have passed STAT 3515Q.

Grading Basis: Graded

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.

View Classes »### 5525. Sampling Theory

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

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.

View Classes »### 5535. Nonparametric Methods

3.00 credits

Prerequisites: Not open to students who have passed STAT 4875.

Grading Basis: Graded

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.

View Classes »### 5585. Mathematical Statistics I

Also offered as: BIST 5585

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

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.

View Classes »### 5605. Applied Statistics II

Analysis of variance, regression and correlation, analysis of covariance, general liner models, robust regression procedures, and regression diagnostics.

View Classes »### 5665. Applied Multivariate Analysis

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

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.

View Classes »### 5675. Bayesian Data Analysis

3.00 credits

Prerequisites: STAT 5585 and 5685; or instructor consent.

Grading Basis: Graded

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.

View Classes »### 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.

View Classes »### 5725. Linear Models I

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

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.

View Classes »### 5735. Linear Models II

3.00 credits

Prerequisites: STAT 5505, 5605, and 5725; open to Ph.D. students who have passed the Ph.D. Qualifying Exam in Statistics; others with permission.

Grading Basis: Graded

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.

View Classes »### 5825. Applied Time Series

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

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.

View Classes »### 5845. Applied Spatio-Temporal Statistics

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission. Recommended Preparation: STAT 5405 or 5605 or GEOG 5600 or 5610 or ERTH 5150 or equivalent.

Grading Basis: Graded

Applied statistical methodology and computing for spatio-temporal data, including visualization, models, and inferences. Extreme value analysis in spatio-temporal contexts. Focus on models that account for spatio-temporal dependence and inferences that provide appropriate uncertainty measures, with applications to real-world problems using open-source software.

View Classes »### 5915. Statistical Data Science in Action

3.00 credits

Prerequisites: STAT 5405 or instructor consent.

Grading Basis: Graded

Real-world statistical data science practice: problem formulation; integration of statistics, computing, and domain knowledge; collaboration; communication; reproducibility; project management.

View Classes »### 6315. Statistical Inference I

3.00 credits

Prerequisites: Open to Ph.D. students who have passed the Ph.D. Qualifying Exam in Statistics, others with permission.

Grading Basis: Graded

Exponential families, sufficient statistics, loss function, decision rules, convexity, prior information, unbiasedness, Bayesian analysis, minimaxity, admissibility, simultaneous and shrinkage estimation, invariance, equivariant estimation.

View Classes »### 6325. Advanced Probability

3.00 credits

Prerequisites: Open to Ph.D. students who have passed the Ph.D. Qualifying Exam in Statistics, others with permission.

Grading Basis: Graded

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.

View Classes »### 6494. Seminar in Applied Statistics

1.00 - 6.00 credits | May be repeated for a total of 24 credits.

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

### 6515. Statistical Inference II

3.00 credits

Prerequisites: STAT 6315; open to Ph.D. students who have passed the Ph.D. Qualifying Exam in Statistics, others with permission.

Grading Basis: Graded

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.

View Classes »### 6615. Statistical Learning and Optimization

Also offered as: BIST 6615

3.00 credits

Prerequisites: Instructor consent and intermediate courses in mathematical and applied statistics.

Grading Basis: Graded

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.

View Classes »### 6694. Seminar in Multivariate Statistics

1.00 - 6.00 credits | May be repeated for a total of 12 credits.

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded

### 6894. Seminar in the Theory of Probability and Stochastic Processes

1.00 - 6.00 credits | May be repeated for a total of 12 credits.

Prerequisites: Open to graduate students in Statistics, others with permission.

Grading Basis: Graded