Courses
Course descriptions and schedules offered below are a helpful guide to find courses offered by the department.
This listing is not meant to replace the official course catalog maintained by the University Registrar.
STAT 1350.01
Elementary Statistics
Introduction to probability and statistics, experiments, and sampling, data analysis and interpretation.
Prereq: Math 1050, or Math Placement Level S, or permission of instructor. Not open to students with credit for GE Data Analysis course (AEDEcon 2005, AnimSci 2260, Astron 3350, Chem 2210, 2210H, ComLdr 3537, EarthSc 2245, Econ 3400, ENR 2000, Geog 2200.01, 2200.02, HCS 2260, IntStds 3400, Ling 2051, 2051H, Philos 1520, Physics 3700, PolitSc 3780, 4781, Sociol 3549, Stat 1350, 1350.01, 1430, 1430.01, 1430.02, 1430H, 1450, 1450.01, 1450.02, 2450, 2450.01, 2450.02, 2480, 2480.01, 2480.02, 3450, 3450.01, 3450.02, 3460, 3470, 3470.01, 3470.02, 4202, 5301, or 5302). GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 1350.02
Elementary Statistics
Introduction to probability and statistics, experiments, and sampling, data analysis and interpretation. Offered online.
Prereq: Math 1050, or Math Placement Level S, or permission of instructor. Not open to students with credit for GE Data Analysis course (AEDEcon 2005, AnimSci 2260, Astron 3350, Chem 2210, 2210H, ComLdr 3537, EarthSc 2245, Econ 3400, ENR 2000, Geog 2200.01, 2200.02, HCS 2260, IntStds 3400, Ling 2051, 2051H, Philos 1520, Physics 3700, PolitSc 3780, 4781, Sociol 3549, Stat 1350, 1350.01, 1430, 1430.01, 1430.02, 1430H, 1450, 1450.01, 1450.02, 2450, 2450.01, 2450.02, 2480, 2480.01, 2480.02, 3450, 3450.01, 3450.02, 3460, 3470, 3470.01, 3470.02, 4202, 5301, or 5302). GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 1430.01
Statistics for the Business Sciences
Fundamentals of probability and statistics: Data collection and summaries, random variables, simple linear regression, two-way tables, conditional probability, sampling distributions, confidence intervals, hypothesis tests, analysis of variance. In-person recitation.
Prereq: Math 1131, 1141, 1151, 1156, 1161.xx, or 1181H, or permission of instructor. Not open to students with credit for 1430, 1430.02, or BusMgt 2320. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 1430.02
Statistics for the Business Sciences
Fundamentals of probability and statistics: Data collection and summaries, random variables, simple linear regression, two-way tables, conditional probability, sampling distributions, confidence intervals, hypothesis tests, analysis of variance. Partly or fully offered online.
Prereq: Math 1131, 1141, 1151, 1156, 1161.xx, or 1181H, or permission of instructor. Not open to students with credit for 1430, 1430.01, or BusMgt 2320. GE data anyl course. GE foundation math and quant reasoning or data anyl course.
STAT 1430H
Statistics for the Business Sciences
Calculus-based fundamentals of probability and statistics: Data collection and summaries, random variables, simple linear regression, two-way tables, conditional probability, sampling distributions, confidence intervals, hypothesis tests, ANOVA.
Prereq: Honors standing, and Math 1131, 1151, 1156, 1161.xx, or 1181H; or permission of instructor. Not open to students with credit for 1430 or BusMgt 2320. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 1450.01
Introduction to the Practice of Statistics
Algebra-based introduction to data analysis, experimental design, sampling, probability, inference, and linear regression. Emphasis on applications, statistical reasoning, and data analysis using statistical software.
Prereq: Math 1116 or 1130 or above, or Math Placement Level L or M, or permission of instructor. Not open to students with credit for 1450, 1450.02, 2450, 2450.01, 2450.02, 2480, 2480.01, or 2480.02. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 1450.02
Introduction to the Practice of Statistics
Algebra-based introduction to data analysis, experimental design, sampling, probability, inference, and linear regression. Emphasis on applications, statistical reasoning, and data analysis using statistical software. Offered online.
Prereq: Math 1116 or 1130 or above, or Math Placement Level L or M, or permission of instructor. Not open to students with credit for 1450, 1450.01, 2450, 2450.01, 2450.02, 2480, 2480.01, or 2480.02. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 1550
Introduction to Statistical Reasoning
Introduction to statistical reasoning through data and application examples, including an introduction to coding in the R software; intended for students considering the Statistics major.
Prereq or concur: Math 1152, or permission of instructor. GE data anyl course.
STAT 2450
Introduction to Statistical Analysis I
Calculus-based introduction to statistical data analysis. Includes sampling, experimental design, probability, binomial and normal distributions, sampling distributions, inference, regression, ANOVA, two-way tables. Prereq: Math 1131, 1151 (152.xx), 1156, 1161.xx, or 1181H, or equiv, or permission of instructor. Not open to students with credit for 245. GE data anly course.
STAT 2450.01
Introduction to Statistical Analysis I
Calculus-based introduction to statistical data analysis. Includes sampling, experimental design, probability, binomial and normal distributions, sampling distributions, inference, regression, ANOVA, two-way tables.
Prereq: Math 1131, 1141, 1151, 1156, 1161.xx, or 1181H, or equiv, or permission of instructor. Not open to students with credit for 2450, 2450.02, 2480, 2480.01, 2480.02, or 3202. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 2450.02
Introduction to Statistical Analysis I
Calculus-based introduction to statistical data analysis. Includes sampling, experimental design, probability, binomial and normal distributions, sampling distributions, inference, regression, ANOVA, two-way tables. Offered online.
Prereq: Math 1131, 1141, 1151, 1156, 1161.xx, or 1181H, or equiv, or permission of instructor. Not open to students with credit for 2450, 2450.01, 2480, 2480.01, 2480.02, or 3202. GE data anyl course. GE foundation math and quant reasoning or data anyl course.
STAT 2460H
Introduction to Statistical Analysis II
Introductory statistics review; Simple linear regression; Multiple regression; One-way ANOVA review; Multiple comparisons; Two-way ANOVA; Bootstrap and permutation tests; Nonparametric tests; Intro to quality/process control; Intro time series.
Prereq: 2450 (245), or permission of instructor. Not open to students with credit for 5301 (528), 5302 (529), 246, or 530.
STAT 2480
Statistics for the Life Sciences
Calculus-based introduction to the statistical analysis of biological data, including probability, common discrete and continuous distributions, experimental design, hypothesis testing, linear regression and correlation. Prereq: Math 1131, 1151 (152), 1156, 1161.XX, or 1181H, or equiv, or permission of instructor. Not open to students with credit for 2450 (245) or 218. GE data anly course.
STAT 2480.01
Statistics for the Life Sciences
Calculus-based introduction to the statistical analysis of biological data, including probability, common discrete and continuous distributions, experimental design, hypothesis testing, linear regression and correlation.
Prereq: Math 1131, 1141, 1151, 1156, 1161.xx, or 1181H, or equiv, or permission of instructor. Not open to students with credit for 2450, 2450.01, 2450.02, 2480, 2480.02, or 3202. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 2480.02
Statistics for the Life Sciences
Calculus-based introduction to the statistical analysis of biological data, including probability, common discrete and continuous distributions, experimental design, hypothesis testing, linear regression and correlation. Offered online.
Prereq: Math 1131, 1141, 1151, 1156, 1161.xx, or 1181H, or equiv, or permission of instructor. Not open to students with credit for 2450, 2450.01, 2450.02, 2480, 2480.01, or 3202. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 2510.01
Statistics in the Sports World
Ask and answer questions, debate issues and analyze data from your favorite sports using statistics. Statistical techniques include contingency tables, regression, estimation, confidence levels, testing. Cannot be used to replace a GE data anly course.
Prereq: One GE data anly course, or equiv, or permission of instructor. Not open to students with credit for 201.01.
STAT 2510.02
Statistics in the Environmental Sciences
Learn, discuss, and apply statistical methods to important problems in the environmental sciences. Statistical techniques will be introduced and illustrated through applications in climate change, pollution monitoring, and biodiversity/conservation.
Prereq: One GE data anly course, or equiv, or permission of instructor. Not open to students with credit for 200.01. Cannot be used to replace a GE data anly course.
STAT 3201
Introduction to Probability for Data Analytics
An introduction to probability and its role in statistical methods for data analytics. Equal emphasis is placed on analytical and simulation-based methods for quantifying uncertainty. Approaches to assessing the accuracy of simulation methods are discussed. Applications of probability and sampling to big-data settings are discussed.
Prereq: Math 1152, 1161.xx, 1172, 1181, or equiv; or permission of instructor. Not open to students with credit for 4201 or Math 4530.
STAT 3202
Introduction to Statistical Inference for Data Analytics
Foundational inferential methods for learning about populations from samples, including point and interval estimation, and the formulation and testing of hypotheses. Statistical theory is introduced to justify the approaches. The course emphasizes challenges that arise when applying classical ideas to big data, partially through the use of computational and simulation techniques.
Prereq: C- or better in 3201, or permission of instructor. Not open to students with credit for 4202.
STAT 3301
Statistical Modeling for Discovery I
Statistical models for data analysis in the linear regression framework. The challenges of developing meaningful models for data are explored, with emphasis on the model building process, the use of numerical and graphical diagnostics for assessing model fit, and interpretation and communication of results. Statistical foundations are introduced along with basic inferential techniques.
Prereq: C- or above in 3202; or 4202 and 5730; or permission of instructor. Prereq or concur: Math 2568, or permission of instructor.
STAT 3302
Statistical Modeling for Discovery II
This course investigates advanced statistical models for data analysis. The regression methods developed in Stat 3301 are extended to data settings with binary and multi-category outcomes. Commonly used statistical methods for exploring and analyzing multivariate data are introduced. Interpretation and communication of the results of analyses is emphasized.
Prereq: C- or above in 3301, and Math 2568 or 5520H; or permission of instructor.
STAT 3303
Bayesian Analysis and Statistical Decision Making
Introduction to concepts and methods for making decisions in the presence of uncertainty. Topics include: formulation of decision problems and quantification of their components; learning about unknown features of a decision problem based on data via Bayesian analysis; characterizing and finding optimal decisions. Techniques and computational methods for practical implementation are presented.
Prereq: C- or above in 3301, or permission of instructor.
STAT 3410
Principles of Data Collection and Analysis
Principles of designing experiments; analysis of variance techniques for hypothesis testing; simultaneous confidence intervals; block designs; factorial experiments; random effects and mixed models; observational data.
Prereq: 3202; or 4202 and 5730; or permission of instructor.
STAT 3440
Statistics in Quality
Descriptive statistics; introduction to probability; Bayes theorem; discrete and continuous random variables, expected value, probability distributions; interval estimation for means and proportions; hypotheses tests for means and proportions; least squares regression; one- and two-way anova; control charts; process capability indices.
Prereq: Math 1152, 1154, 1155, 1161.xx, 1172, or equiv, or permission of instructor. Not open to students with credit for 3450, 3450.01, 3450.02, 3460, 3470, 3470.01 or 3470.02.
STAT 3450
Basic Statistics for Engineers
Introduction to probability; Normal distribution; Confidence intervals for means; Hypothesis tests for means; Multi-factor experiments; Experiments with blocking. Prereq: Math 1152, 1161.xx, 1172, or 1181, or equiv, or permission of instructor. Not open to students with credit for 3460 or 3470. GE data anly course.
STAT 3450.01
Basic Statistics for Engineers
Introduction to probability; Normal distribution; Confidence intervals for means; Hypothesis tests for means; Multi-factor experiments; Experiments with blocking.
Prereq: Math 1152, 1161.xx, 1172, or 1181, or equiv, or permission of instructor. Not open to students with credit for 3440, 3450, 3450.02, 3460, 3470, 3470.01, or 3470.02. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 3450.02
Basic Statistics for Engineers
Introduction to probability; Normal distribution; Confidence intervals for means; Hypothesis tests for means; Multi-factor experiments; Experiments with blocking. Offered online.
Prereq: Math 1152, 1161.xx, 1172, or 1181, or equiv, or permission of instructor. Not open to students with credit for 3440, 3450, 3450.01, 3460, 3470, 3470.01, or 3470.02. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 3460
Principles of Statistics for Engineers
Introduction to probability, random variables, distributions, expected values; confidence intervals; paired and unpaired t-tests; linear regression; analysis of variance; blocked experiments; fractional factorial experiments; quality control charts.
Prereq: Math 1152, 1161.xx, 1172, 1181H, 153, or 254, or equiv, or permission of instructor. Not open to students with credit for 3450, 3470, 427, or 428. GE data anly course.
STAT 3470.01
Introduction to Probability and Statistics for Engineers
Introduction to probability, Bayes theorem; discrete and continuous random variables, expected value, probability distributions; point and interval estimation; hypotheses tests for means and proportions; least squares regression.
Prereq: Math 1152, 1161.xx, 1172, 1181H, or equiv, or permission of instructor. Not open to students with credit for 3440, 3450, 3450.01, 3450.02, 3460, 3470, or 3470.02. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 3470.02
Introduction to Probability and Statistics for Engineers
Introduction to probability, Bayes theorem; discrete and continuous random variables, expected value, probability distributions; point and interval estimation; hypotheses tests for means and proportions; least squares regression. Offered online.
Prereq: Math 1152, 1161.xx, 1172, 1181H, or equiv, or permission of instructor. Not open to students with credit for 3440, 3450, 3450.01, 3450.02, 3460, 3470, or 3470.01. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 4193
Individual Studies
Individual conferences, assigned readings, and reports on minor investigations.
Prereq: Permission of instructor. Repeatable to a maximum of 15 cr hrs or 5 completions. This course is graded S/U.
STAT 4194
Group Studies
Designed to give groups of students an opportunity to pursue special studies not otherwise offered.
Prereq: Permission of instructor. Repeatable to a maximum of 15 cr hrs or 3 completions.
STAT 4201
Introduction to Mathematical Statistics I
Basic concepts in mathematical statistics, including probability, discrete and continuous distributions and densities, mathematical expectation, functions of random variables, transformation techniques, sampling distributions, order statistics.
Prereq: C- or better in Math 2153, 2162.xx, 2182H, or 4182H, or permission of instructor. Not open to students with credit for 3201, 4202, 6201, 6301, 6801, Math 4530 or 5530H.
STAT 4202
Introduction to Mathematical Statistics II
Decision theory, point and interval estimation, Neyman-Pearson lemma, likelihood ratio tests, tests for means, variances, and proportions, nonparametric tests, regression, and ANOVA.
Prereq: C- or better in 4201, Math 4530, or 5530H, or permission of instructor. Not open to students with credit for 3202, 6201, 6302, or 6802. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 4301
Advanced Statistical Inference
Advanced probability models and fundamentals of inferential procedures; distribution functions, moment generating functions, transformations, order statistics, large-sample theory, classical hypothesis testing, distribution-free hypothesis tests.
Prereq: 3201 and 3202, or 4201 and 4202; and Math 2153; or permission of instructor.
STAT 4302
Computational Statistics
Topics in computational statistics using the R software, including design and execution of classical and modern Monte Carlo experiments, and statistical inference based on resampling methods, such as bootstrap, jackknife, and permutation.
Prereq: 3301 and 4301, or permission of instructor.
STAT 4620
Introduction to Statistical Learning
The course provides an introduction to the principles of statistical learning and standard learning techniques for regression, classification, clustering, dimensionality reduction, and feature extraction.
Prereq: C- or better in 3302, or permission of instructor.
STAT 4690
Undergraduate Topics in Statistics
Various topics in Statistics and Data Analysis that are relevant to an undergraduate audience. Topics vary per offering.
Repeatable to a maximum of 12 cr hrs or 3 completions.
STAT 4911
Data Analytics Capstone
A teamwork-based synthesis of the Data Analytics major curriculum through the analysis of data supplied by a partnering institution. Prepares students for the complexity of data analysis they will encounter outside of the university in a mentored setting.
Prereq: 4620, or permission of instructor.
STAT 4998
Undergraduate Research in Statistics
Designed to give undergraduates experience in carrying out statistics research.
Prereq: Permission of instructor. Repeatable to a maximum of 30 cr hrs or 6 completions. This course is graded S/U.
STAT 4999
Undergraduate Thesis Research in Statistics
Designed to give undergraduates experience in carrying out statistics research.
Prereq: Permission of instructor. Repeatable to a maximum of 30 cr hrs or 6 completions. This course is graded S/U.
STAT 5301
Intermediate Data Analysis I
The first course in a two-semester non-calculus sequence in data analysis covering descriptive statistics, design of experiments, probability, statistical inference, one-sample t, goodness of fit, two sample problem, and one-way ANOVA.
Prereq: Math 1075 or equiv, or Math Placement Level of R, or permission of instructor. Not open to students with credit for 5302. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 5302
Intermediate Data Analysis II
The second course in a two-semester sequence in data analysis covering simple linear regression (inference, model diagnostics), multiple regression models, variable selection, model selection, two-way ANOVA, mixed effects model.
Prereq: 5301, or permission of instructor. GE data anly course. GE foundation math and quant reasoning or data anyl course.
STAT 5510
Statistical Foundations of Survey Research
Understand and practice methods of survey research and data analysis including questionnaire design and pilot testing, non-sampling and sampling errors, sampling design, descriptive statistics, estimation, and hypothesis testing; and ethics.
Prereq: 1350 (135), 1450 (145), or 5301 (528), and Math 1075 (104), or equiv; or permission of instructor. Not open to students with credit for 6510 (651) or 551.
STAT 5550
Introductory Time Series Analysis
Introduces the statistical methodology and models to analyze time series data in practice.
Prereq: 3301; or 4202 and 5302; or permission of instructor. Not open to students with credit for 6550 (635) or 7550.
STAT 5730
Introduction to R for Data Science
Introduces underlying concepts of the R programming language and R package ecosystem for manipulation, visualization, and modeling of data, and for communicating the results of and enabling replication of their analyses.
Prereq: 1350, 1350.01, 1350.02, 1450, 1450.01, 1450.02, 1550, 2450, 2450.01, 2450.02, 2480, 2480.01, 2480.02, 3201, 3202, 3450, 3450.01, 3450.02, 3460, 3470, 3470.01, 3470.02, 4202, 5301, or 5302, or equiv., or permission of instructor.
STAT 5731
Introduction to R for Data Science I: Basic R
The first course in a sequence designed for teaching students how to use R effectively for doing data science. This course introduces the basic flow and focuses on basic usage of important tools in R for visualization, transformation, and organization of data.
Prereq: 1350, 1350.01, 1350.02, 1430, 1430.01, 1430.02, 1450, 1450.01, 1450.02, 1550, 2450, 2450.01, 2450.02, 2480, 2480.01, 2480.02, 3201, 3202, 3450, 3450.01, 3450.02, 3460, 3470, 3470.01, 3470.02, 4202, 5301, or 5302, or equiv., or graduate standing, or permission of instructor. Not open to students with credit for Stat 5730.
STAT 5732
Introduction to R for Data Science II: Intermediate R
The second course in a sequence designed for teaching students how to use R effectively for doing data science. This course dives deeper into tools in R at an intermediate level that is beneficial for complex projects and analyses, as well as fundamental programming concepts including basic and special data types, functions, and iteration.
Prereq: 5731; or permission of the instructor. Not open to students with credit for Stat 5730.
STAT 5740
Introduction to SAS Software
The basic statistical procedures covered will be illustrated using SAS. The intent of the course is to cover some of the SAS statistical methods that graduate students from outside the Statistics Department require for their own research.
Prereq: 3202 or 4202 or 5301, or permission of instructor.
STAT 5760
Statistical Consulting Support from the SCS
Graduate or undergraduate students enrolled in this course will work with a graduate student consultant employed by the Statistical Consulting Service for the purpose of making progress on their thesis or dissertation.
Repeatable to a maximum of 15 cr hrs. This course is graded S/U.
STAT 6040
Mentored Teaching Experience in Statistics
The application of best pedagogical practices in selected statistics teaching experiences. A supervised teaching component is included.
Prereq: Grad standing in Statistics or Biostatistics, and permission of instructor. Not open to students with credit for 604. This course is graded S/U.
STAT 6111
Foundations of Statistical Theory I
This is the first part of a course that reviews and introduces the mathematical foundations that are necessary for the coursework in the PhD programs in statistics and biostatistics and the statistics MS program, focusing on using mathematical tools for statistical analysis.
Prereq: Grad standing in the Statistics MS program, Statistics PhD program, or Interdisciplinary Biostatistics PhD program; or permission of instructor.
STAT 6112
Foundations of Statistical Theory II
This is the second part of a course that reviews and introduces the mathematical foundations that are necessary for the coursework in the PhD programs in statistics and biostatistics and the statistics MS program, focusing on using mathematical tools for statistical analysis.
Prereq: Grad standing in the Statistics MS program, Statistics PhD program, or Interdisciplinary Biostatistics PhD program; or permission of instructor.
STAT 6193
Individual Studies in Foundational Graduate Topics in Statistics
Individual conferences, assigned readings, and reports on minor investigations in foundational graduate topics in Statistics.
Prereq: Permission of instructor. Repeatable to a maximum of 20 cr hrs or 5 completions. This course is graded S/U.
STAT 6194
Group Studies in Foundational Graduate Topics in Statistics
Designed to give groups of students an opportunity to pursue special studies in foundational graduate topics in Statistics not otherwise offered.
Prereq: Permission of instructor. Repeatable to a maximum of 15 cr hrs or 3 completions. This course is graded S/U.
STAT 6201
Mathematical Statistics
Probability, random variables, expectation, moment generating functions, discrete and continuous distributions, limit theorems, maximum likelihood and Bayesian estimation, confidence intervals, hypothesis tests, Neyman-Pearson lemma, t and F tests.
Prereq: Math 2153 or equiv, or permission of instructor. Not open to students with credit for 6301 or 6801.
STAT 6301
Probability for Statistical Inference
Introduction to probability, random variables, and distribution theory; intended primarily for students in MAS degree program.
Prereq: Math 4547 (548), or permission of instructor. Not open to students with credit for 6801 (620 or 621), Math 4530 (530), or 5530H (531).
STAT 6302
Theory of Statistical Analysis
Estimation, hypothesis tests, best tests, likelihood ratio tests, confidence sets, sufficiency, efficient estimators; intended primarily for students in the MAS degree program.
Prereq: 6301 (610) or 6801 (620), or permission of instructor. Not open to students with credit for 6802 (621, 622, or 623).
STAT 6410
Design and Analysis of Experiments
Principles of designing experiments; analysis of variance techniques for hypothesis testing, simultaneous confidence intervals; block designs, factorial experiments, random effects and mixed models, split plot designs, response surface design.
Prereq: 6201 (521), 6302 (623), or 6802 (622), and 6450 (645) or 6950; or permission of instructor. Not open to students with credit for 6910 (641).
STAT 6450
Applied Regression Analysis
Simple and multiple linear regression, diagnostics, model selection, models with categorical variables.
Prereq: 6201, or equiv, or permission of instructor. Not open to students with credit for 6950.
STAT 6500
Statistical Machine Learning
Statistical models and algorithms for supervised and unsupervised learning; linear and logistic regression; classification and LDA; cross-validation and bootstrap; variable selection; ridge and LASSO penalization; smoothing splines and GAMs; SVM and kernels; CART and random forests; bagging; boosting; feed-forward and convolutional neural networks; k-means clustering and Gaussian mixtures; PCA.
Prereq: 6450, or permission of instructor. Not open to students with credit for 7620.
STAT 6510
Survey Sampling Methods
Sampling from finite populations, simple random, stratified, systematic and cluster sampling design, ratio and regression estimates, non-sampling errors, models.
Prereq: 5301 (529) or PubHBio 6212 (703), or equiv. Not open to students with credit for 651 or PubHBio 7225 (651). Cross-listed in PubHBio 7225.
STAT 6520
Applied Statistical Analysis with Missing Data
Models and methods for the dataset with missing values, including imputation, likelihood-based, and Bayesian models.
Prereq: 6201, 6302 (623), or 6802 (622), and 6450 (645), 6950, PubHBio 6203, or 703; or permission of instructor. Not open to students with credit for 6520 (652) or PubHBio 7240 (652). Cross-listed in PubHBio 7240.
STAT 6530
Introduction to Spatial Statistics
Provides an introduction to spatial statistical methods based on the viewpoint that spatial data are a realization from a random process.
Prereq: 6450 (645), 6950, or Geog 883.02, or permission of instructor. Not open to students with credit for 8530 (829) or 631.
STAT 6540
Applied Stochastic Processes
An introduction to some of the most commonly encountered stochastic processes. Goals include understanding basic theory as well as applications. Students should be familiar with basic probability, including conditional probability and expectation.
Prereq: 6301 (610) or equiv, or permission of instructor. Not open to students with credit for 632.
STAT 6550
The Statistical Analysis of Time Series
To develop knowledge of time series processes, modeling (identification, estimation, and diagnostics), and forecasting methods. Experience is gained in the statistical theory so as to be able to analyze time series data in practice.
Prereq: 6201, 6302, or 6802, and 6450 or 6950; or permission of instructor.
STAT 6560
Applied Multivariate Analysis
An introduction to classical multivariate statistical methods based on the multivariate normal distribution. Sufficient matrix algebra will be covered to enable students to understand multivariate methods using matrix notation.
Prereq: 6450 (645) or 6950, or equiv, or Math 2568 (568), or equiv, or permission of instructor. Not open to students with credit for 656.
STAT 6570
Applied Bayesian Analysis
Introduces various aspects of Bayesian modeling (including conditionally specified models and models for non-normal data) and simulation-based model-fitting strategies.
Prereq: 6301 (610) or 6801 (621 and 622), or permission of instructor. Prereq or concur: 6450 (645) or 6950, and 6302 (623) [with 6301 prerequisite] or 6802 [with 6801 prerequisite]; or permission of instructor. Not open to students with credit for 625.
STAT 6605
Applied Survival Analysis
Introduction to time-to-event data analysis. Topics include summary statistics, non-parametric methods, semiparametric and parametric models, and competing risks analysis. Focus is on analysis of health data using statistical software.
Prereq: 6450, 6950, or PubHBio 6211. Not open to students with credit for 6605 or PubHBio 7235. Cross-listed in PubHBio 7235.
STAT 6610
Applied Nonparametric Statistics II
Noncalculus treatment of nonparametric tests, confidence intervals, estimation; topics include one- and two-sample problems, one- and two-way analysis of variance, multiple comparisons, correlation.
Prereq: 5301, 6201, or 6302, or equiv, or permission of instructor.
STAT 6615
Design and Analysis of Clinical Trials
Design, monitoring, and analysis of clinical trials; includes protocol development, randomization schemes, sample size methods, and ethical issues.
Prereq: 5301 (528 and 529), or equiv, or permission of instructor. Not open to students with credit for Biostat 615 or PubHBio 7215. Cross-listed in PubHBio 7215.
STAT 6620
Environmental Statistics
Survey of statistical methods for environmental data, with a focus on applications. Topics include sampling, regression, censoring, risk analysis, bioassay, time series, spatial statistics, and environmental extremes.
Prereq: 5302 (529) or 6450 (645) or 6910 or Geog 683.xx or 833.01; prereq or concur: Stat 6910; or permission of instructor. Not open to students with credit for 662.
STAT 6625
Statistical Analysis of Genetic Data
Introduction to Mendelian principles, genetic epidemiology, and molecular genetics; family studies; model-based and model-free linkage analysis for mapping disease genes; genome wide association studies; association analysis using haplotypes.
Prereq: 6301 (610) and 6302 (623), or permission of instructor.
STAT 6640
Principles of Statistical Quality Control
Statistical quality control. Topics include basic concepts, common control charts for quantitative and qualitative data, graphical techniques, process capability studies, and selected additional material as time permits.
Prereq: 6201 (521), 6302 (623), or 6802 (622), or equiv, or permission of instructor. Not open to students with credit for 664.
STAT 6650
Discrete Data Analysis
Two-by-two tables; cross-sectional, prospective, and retrospective studies; measures and tests of association; log linear models; association graphs; analysis of stratified tables.
Prereq: 5302 (530), 6450 (645), 6950, PubHBio 6203, or 703, or permission of instructor. Not open to students with credit for 665.
STAT 6690
Foundational Graduate Topics in Statistics
Various foundational topics in Statistics and Data Analysis that are relevant to a graduate audience. Topics vary per offering.
Prereq: Permission of instructor. Repeatable to a maximum of 12 cr hrs or 3 completions.
STAT 6730
Introduction to Computational Statistics
Introduction to computational statistics. Students will learn how to manipulate data, perform statistical analyses, perform simple Monte Carlo experiments, use resampling methods and discuss the results obtained from their analyses.
Prereq: 6301 (610), 6302 (623), and 6410 (641) or 6910, and 6450 (645) or 6950; or permission of instructor. Not open to students with credit for 673.
STAT 6740
Data Management and Graphics for Statistical Analyses
Data manipulation for statistical analyses, missing data calculations, merging and transporting data sets, formatting data analysis results, using relational databases and SQL, character data, graphical presentation of data, and macro programming. No prior knowledge of SAS programming is required.
Prereq: Not open to students with credit for 5740.
STAT 6750
Statistical Consulting and Collaboration
Role of the statistician as both consultant and collaborator; enhancement of analytical and communication skills; structuring working engagements; introduction to consulting-specific technical skills; experience working on consulting projects.
Prereq: 6450 (645) or 6950, or permission of instructor. Not open to students with credit for 600 or 601. This course is graded S/U.
STAT 6801
Statistical Theory I
Introduction to probability, random variables, distribution theory and principles of inference. Intended primarily for students in the PhD program in Statistics or Biostatistics.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Not open to students with credit for 6301 (610) or 620.
STAT 6802
Statistical Theory II
Introduction to statistical inference: Estimation, hypothesis testing, confidence intervals, and decision theory. Intended primarily for students in the PhD program in Statistics or Biostatistics.
Prereq: 6801, or permission of instructor. Not open to students with credit for 622.
STAT 6860
Foundations of the Linear Model
Linear models; Least squares estimates; Multivariate normal distribution; Maximum likelihood estimators; Covariance matrices, information matrices; Quadratic forms; Principal components; Orthogonal polynomial regression; Non-central distributions.
Prereq: Math 2568 (568) or equiv, or permission of instructor. Concur: 6802. Not open to students with credit for 742.
STAT 6910
Applied Statistics II
One and two-sample problems, randomization-based inference, contingency tables, analysis of variance, the mixed model, experimental designs. Intended primarily for students in the PhD program in Statistics or Biostatistics.
Prereq or concur: 6801, or permission of instructor. Not open to students with credit for 6410.
STAT 6950
Applied Statistics I
One and two-sample problems, exploratory data analysis, simple and multiple linear regression, diagnostics and model selection. Intended primarily for students in the PhD program in Statistics or Biostatistics.
Coreq: Stat 6801, or permission of instructor. Not open to students with credit for 6450.
STAT 6998
Research in Foundational Graduate Topics in Statistics
Research topics in foundational graduate topics in Statistics.
Prereq: Permission of instructor. Repeatable to a maximum of 30 cr hrs or 6 completions. This course is graded S/U.
STAT 7201
Theory of Probability
Measure and integration, random variables, independence, integration and expectation, convergence, characteristic functions, central limit theorems. Intended primarily for students in the PhD program in Statistics or Biostatistics.
Prereq: 6802 (622), or permission of instructor. Not open to students with credit for 722 or 723.
STAT 7301
Advanced Statistical Theory
Fundamental concepts from mathematical statistics, derivation/classification of estimators, large sample asymptotic analysis, and non-asymptotic analysis of high-dimensional estimation. Intended for Ph.D. students in Statistics or Biostatistics.
Prereq: 6802, or permission of instructor.
STAT 7302
Bayesian Analysis and Decision Theory
Decision theory, loss functions, priors, posteriors, Bayesian inference, empirical Bayes, hierarchical modeling, computation, and Bayesian model assessment and robustness. Intended primarily for PhD students in Statistics or Biostatistics.
Prereq: 7301, or permission of instructor. Not open to students with credit for 7303.
STAT 7303
[Archived] Bayesian Analysis and Decision Theory
This course is no longer offered. See STAT 7302 for related course content.
STAT 7410
Linear Models
Theory of the general linear model, definition, assumptions, estimability, hypothesis testing and multiple comparisons. Modern extensions of the linear model, interpretation, inference, prediction and penalized estimation. Multivariate linear models.
Prereq: 6802, 6910, and 6950; or permission of instructor.
STAT 7430
Generalized Linear Models
Introduces the statistical theory and methods to extend regression and analysis of variance to non-normal data. Students will learn to use fixed and random effect generalized linear models to model univariate and multivariate data.
Prereq: 6801, 6802, 6910, 6950, and 7410; or permission of instructor.
STAT 7470
Advanced Longitudinal Data Analysis
Classical and modern statistical approaches for continuous and discrete longitudinal data. Random effects and growth curve models, measurement error, generalized estimating equations, estimation with missing data, multivariate longitudinal data.
Prereq: 6802 (622) and 6950 (645), or permission of instructor. Not open to students with credit for 726 or PubHBio 8230. Cross-listed in PubHBio 8230.
STAT 7540
Theory of Stochastic Processes
Markov chains, ergodicity, Poisson process, martingales, Brownian motion, Gaussian processes, diffusion processes. Intended primarily for students in the PhD program in Statistics or Biostatistics.
Prereq: 7201, or permission of instructor. Not open to students with credit for 832.
STAT 7550
Time Series Theory and Methods
A systematic advanced treatment of areas of current interest in the statistical theory and methods for the analysis of time series processes. Topics will be announced each semester.
Prereq: 6560 (656) or 6860, 6801 (620 and 621), 6802 (621 and 622), and 6950 (645); or permission of instructor.
STAT 7560
Multivariate Analysis
Matrix normal distribution; Matrix quadratic forms; Matrix derivatives; The Fisher scoring algorithm. Multivariate analysis of variance; Random coefficient growth models; Principal components; Factor analysis; Discriminant analysis; Mixture models.
Prereq: 6802 (622), or permission of instructor. Not open to students with credit for 755 or 756.
STAT 7605
Advanced Regression Modeling of Time-to-Event Data
Advanced topics in survival analysis. Proportional hazards models, parametric regression models, length-bias and prevalent sampling, multivariate survival analysis, counting processes, recurrent events.
Prereq: 6802 (622) and 6950. Not open to students with credit for PubHBio 8235 or 706. Cross-listed in PubHBio 8235.
STAT 7610
Theory of Nonparametric Statistics
Theory of distribution-free statistics based on counting and ranking; U-statistics; univariate and multivariate rank regression; additional topics on nonparametric statistics.
Prereq: 6802 (622), or permission of instructor. Not open to students with credit for 761.
STAT 7620
Elements of Statistical Learning
Statistical and Machine Learning - Applied modern regression, pattern recognition and clustering techniques for discovery/understanding of underlying statistical structures within large, complex and noisy data sets.
Prereq: 6301 (610) and 6302 (623), or 6801 (620) and 6802 (622), or ECE 6001, or 7001, or equiv; or permission of instructor. Not open to students with credit for 760.
STAT 7630
Nonparametric Function Estimation
Function estimation with emphasis on smoothing splines, flexible model building with multivariate data, reproducing kernel Hilbert space methods, additional topics in smoothing.
Prereq: 6802 and 6950, or permission of instructor.
STAT 7730
Advanced Computational Statistics
Covers modern methods of statistical computing, with emphasis on how and why they work. As a prerequisite, students should be able to program basic functions. Intended primarily for students in the PhD program in Statistics or Biostatistics.
Prereq: 6802 (622) and 6950 (645); or permission of instructor. Not open to students with credit for 773.
STAT 7755
Biostatistical Collaboration
Basic biomedical research methodologies; collaborate with biomedical researchers to design experiments and plan analyses; protocol preparation; professional skills development; statistical report preparation.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Not open to students with credit for 709. This course is graded S/U. Cross-listed in PubHBio 7245.
STAT 7789
Survey Research Practicum
Hands-on applications for students interested in the planning, implementation, and analysis of a scientific sample survey.
Prereq: Admission to Grad interdisciplinary specialization in survey research, or permission of instructor. Not open to students with credit for 7789 or 789 in AEDEcon, AgrEduc, BusML, Comm, Econ, EduPL, Geog, PolitSc, Psych, PubHlth, PubAfrs, Sociol, or Stat. Cross-listed in Comm, Econ, and PolitSc.
STAT 7998
Intermediate Graduate Research in Statistics
Research topics in intermediate graduate topics in Statistics.
Prereq: Permission of instructor. Repeatable to a maximum of 12 cr hrs or 3 completions. This course is graded S/U.
STAT 7999
Masters Thesis Research in Statistics
Masters Thesis Research in Statistics.
Prereq: Grad standing in Stat. Repeatable. This course is graded S/U.
STAT 8010
Seminar on Research Topics in Statistics
Lectures on current research by each graduate faculty member in statistics.
Prereq: Grad standing in Statistics. Repeatable to a maximum of 4 cr hrs. This course is graded S/U.
STAT 8193
Individual Studies in Advanced Graduate Topics in Statistics
Individual conferences, assigned readings, and reports on minor investigations in advanced graduate topics in Statistics.
Prereq: Permission of instructor. Repeatable to a maximum of 25 cr hrs or 5 completions. This course is graded S/U.
STAT 8194
Group Studies in Advanced Graduate Topics in Statistics
Designed to give groups of students an opportunity to pursue special studies in advanced graduate topics in Statistics not otherwise offered.
Prereq: Permission of instructor. Repeatable to a maximum of 25 cr hrs or 5 completions. This course is graded S/U.
STAT 8310
Large Sample Theory
Stochastic Convergence, Delta Method, Moment Estimators, M- and Z- estimators, Efficiency of Estimators, U- Statistics, Rank, Sign and Permutation Statistics, Large sample methods for functional data.
Prereq: 7201 (722 and 723) and 7302 (821), or permission of instructor. Not open to students with credit for 888.
STAT 8410
Capstone Applications
Intensive, project-based investigation of applied and/or interdisciplinary statistical problems, suitable for advanced PhD students in Statistics and Biostatistics.
Prereq: 7302 (821), 7410 (742), and 7540 (832), and Grad standing in Statistics or Biostatistics; or permission of instructor.
STAT 8450
Stochastic Epidemic Models
Introduction to methods of analyzing large population epidemic data from the viewpoint of stochastic processes theory. Topics will cover the SIR (susceptible-infective-removed) epidemic models both under the homogenous and restricted contact structures. Lectures will introduce the necessary background in probability and statistics along with real-life applications (e.g. HIV, H1N1 and SARS).
Prereq: 6801, and 6540 or 7540; or permission of instructor. Not open to students with credit for PubHBio 8450. Cross-listed in PubHBio.
STAT 8460
Special Topics in Design of Experiments
Selection of Advanced Topics from: Theory of optimal design; Computational Algorithms; Design and analysis of computer experiments; Design for nonlinear models; Discrete choice experiments.
Prereq: 7410 (742), or permission of instructor. Not open to students with credit for 847.
STAT 8530
Spatial and Spatio-Temporal Statistics
Geostatistics, kriging, hierarchical statistical models, Markov random fields, spatial point processes, spatio-temporal statistical models. Intended primarily for students in the PhD program in Statistics or Biostatistics.
Prereq: 6802 (622) and 6950 (645), or permission of instructor. Not open to students with credit for 829.
STAT 8540
Topics in Advanced Stochastic Processes
Dedicated to advanced topics in stochastic processes, such as stochastic integration and stochastic differential equations (SDEs), numerical methods and inference for SDEs, etc. Applications in several areas will be discussed.
Prereq: 7201 (722 and 723), or permission of instructor.
STAT 8570
Advanced Bayesian Analysis: Modeling
A systematic advanced treatment of areas of current interest in Bayesian analysis. Topics will be announced each semester.
Prereq: 7303 (820), or permission of instructor. Repeatable to a maximum of 6 cr hrs.
STAT 8575
Advanced Bayesian Analysis: Computation
A systematic advanced treatment of areas of current interest in Bayesian analysis. Topics will be announced each semester.
Prereq: 7730 (773) and 7303 (820), or permission of instructor.
STAT 8605
Advanced Survival Analysis
Counting process approach to modeling life history data, including Nelson-Aalen, product limit, and K-sample estimators. Topics from parametric models, semiparametric proportional and additive hazards regressions, and multivariate survival models.
Prereq: 7201 (722 and 723) and 7540 (832), or permission of instructor. Not open to students with credit for Biostat 805 and 806.
STAT 8625
Statistical Methods for Analyzing Genetic Data
Basic principles of population genetics; gene frequency estimation; likelihood computation on pedigrees using peeling algorithm, Lander-Green algorithm, Monte Carlo methods; linkage analysis, population and family based association studies.
Prereq: 6802, or permission of instructor.
STAT 8750.01
Research Group in Statistical Learning and Data Mining
Research group in Statistical Learning and Data Mining. Topics vary by the offering.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 10 cr hrs. This course is graded S/U.
STAT 8750.02
Research Group in Design of Physical and Computer Experiments
Research group in Design of Physical and Computer Experiments. Topics vary by the offering.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 10 cr hrs. This course is graded S/U.
STAT 8750.03
Research Group in Statistical Genetics and Bioinformatics
Research group in Genetics. Topics vary by the offering.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 10 cr hrs. This course is graded S/U.
STAT 8750.04
Research Group in Quantitive Methods in Consumer Behavior
Research group in Quantitive Methods in Consumer Behavior. Topics vary by the offering.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 10 cr hrs. This course is graded S/U.
STAT 8750.05
Research Group in Ranked Set Sampling
Research group in Ranked Set Sampling. Topics vary by the offering.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 10 cr hrs. This course is graded S/U.
STAT 8750.06
Research Group in Spatial Statistics and Environmental Statistics
Research group in Spatial Statistics and Environmental Statistics. Topics vary by the offering.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 10 cr hrs. This course is graded S/U.
STAT 8750.07
Research Group in the Analysis of Clinical Trials Data, including Efficacy, Safety, and Biomarkers
Research group in Analysis of Clinical Trials Data. Topics vary by the offering.
Prereq: Grad standing in Statistics, or permission of instructor. Repeatable to a maximum of 10 cr hrs. This course is graded S/U.
STAT 8750.08
Research Group in Observational Data
Research group in Observational Data. Topics vary by the offering.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 10 cr hrs. This course is graded S/U.
STAT 8810
Advanced Topics in Statistics I
A systematic advanced treatment of areas of current interest in Statistics. Topics will be announced each semester.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 24 cr hrs or 8 completions.
STAT 8820
Advanced Topics in Statistics II
A systematic advanced treatment of areas of current interest in Statistics. Topics will be announced each semester.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 24 cr hrs or 8 completions.
STAT 8830
Advanced Topics in Statistics III
A systematic advanced treatment of areas of current interest in Statistics. Topics will be announced each semester.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 24 cr hrs or 8 completions.
STAT 8840
Advanced Topics in Statistics IV
A systematic advanced treatment of areas of current interest in Statistics. Topics will be announced each semester.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 24 cr hrs or 8 completions.
STAT 8850
Advanced Topics in Biostatistics I
A systematic advanced treatment of areas of current interest in Biostatistics. Topics will be announced each semester.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 24 cr hrs or 8 completions.
STAT 8860
Advanced Topics in Biostatistics II
A systematic advanced treatment of areas of current interest in Biostatistics. Topics will be announced each semester.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 24 cr hrs or 8 completions.
STAT 8895
Statistics Seminar
Topics range over the current research interests of statisticians from around the world; some lectures are of an expository nature.
Prereq: Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 20 cr hrs. This course is graded S/U.
STAT 8998
PhD Dissertation Research in Statistics (Pre-candidacy)
PhD Dissertation research in Statistics (Pre-candidacy).
Prereq: Permission of instructor. Repeatable. This course is graded S/U.
STAT 8999
PhD Dissertation Research in Statistics
PhD Dissertation research in Statistics.
Prereq: Permission of instructor. Repeatable. This course is graded S/U.