For the course catalog under quarters (prior to summer 2012), click here.

**1350 - Elementary Statistics U 3 **[Sample Syllabus]

Introduction to probability and statistics, experiments, and sampling, data analysis and interpretation.*Prereq:* Math 1050 (050), or Math Placement Level S, or permission of instructor. Not open to students with credit for GEC Data Analysis (AEDEcon 205, AEE 387, AnimSci 260, Astron 350, Chem 221, DentHyg 383, EarthSci 245, Econ 443, ENR 222, HCS 260, IntStds 443, Linguist 286, Philos 153, Physics 416, PolitSc 485 (585), SocWork 570, Sociol 549, SphHrng 286, Stat 135, 145, 245, 427, or 520, or semester equivs).

GE data anly course.

**1430 - Statistics for the Business Sciences U 4** [Sample Syllabus]

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.*Prereq:* Math 1131 (132), 1151 (152.xx), 1156, 1161.xx (161.xx), or 1181H (161.xxH), or permission of instructor. Not open to students with credit for 133.

GE data anly course.

**1430H - Statistics for the Business Sciences U 4** [Sample Syllabus]

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 (132), 1151 (152.xx), 1156, 1161.xx (161.xx), or 1181H (161.xxH); or permission of instructor. Not open to students with credit for 133.

GE data anly course.

**1450 - Introduction to the Practice of Statistics U 3** [Sample Syllabus]

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 (116), or 1130 (130) or above, or Math Placement Level L or M, or permission of instructor. Not open to students with credit for 2450 (245) or 145.

GE data anly course.

**2450 - Introduction to Statistical Analysis I U 3** [Sample Syllabus]

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.

**2480 - Statistics for the Life Sciences U 3** [Sample Syllabus]

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.

**3201 - Introduction to Probability for Data Analytics U 3** [Sample Syllabus]

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, or 1181 or equiv; or permission of instructor.

**3202 - Introduction to Statistical Inference for Data Analytics U 4 ** [Sample Syllabus]

The course covers 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.

**3301 - Statistical Modeling for Discovery I U 3 ** [Sample Syllabus]

Statistical models for data analysis and discovery in big-data settings, with primary focus on linear regression models. The challenges of building meaningful models from vast data are explored, and emphasis is placed on model building and the use of numerical and graphical diagnostics for assessing model fit. Interpretation and communication of the results of analyses is emphasized.*Prereq:* C- or better in 3202; or permission of instructor. Prereq or concur: Math 2568; or permission of the instructor.

**3302 - Statistical Modeling for Discovery II U 3 ** [Sample Syllabus]

Statistical models for data analysis and discovery in big-data settings. The regression methods developed in Stat 3301 are extended to data settings with binary and multi-category outcomes. An introduction to some of the most commonly used statistical methods for exploring and analyzing multivariate data is provided. Interpretation and communication of the results of analyses is emphasized.
*Prereq:* C- or better in 3301, and Math 2568; or permission of instructor.

**3303 - Bayesian Analysis and Statistical Decision Making U 3 ** [Sample Syllabus]

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 better in 3202, or permission of instructor.

**3450 - Basic Statistics for Engineers U 2 ** [Sample Syllabus]

Introduction to probability; Normal distribution; Confidence intervals for means; Hypothesis tests for means; Multi-factor experiments; Experiments with blocking.*Prereq:* Math 1152 (153), 1161.xx, 1172 (254), or 1181, or equiv, or permission of instructor. Not open to students with credit for 3460, 3470, 427, or 428.

GE data anly course.

**3460 - Principles of Statistics for Engineers U 3 **[Sample Syllabus]

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.

**3470 - Introduction to Probability and Statistics for Engineers U 3** [Sample Syllabus]

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, 153 or 254, or equiv, or permission of instructor. Not open to students with credit for 3450, 3460, 427, or 428.

GE data anly course.

**4193 - Individual Studies U 1-3**

Individual conferences, assigned readings, and reports on minor investigations. Repeatable to a maximum of 15 cr hrs or 5 completions. This course is graded S/U.

Designed to give groups of students an opportunity to pursue special studies not otherwise offered. Repeatable to a maximum of 15 cr hrs or 3 completions. This course is graded S/U.

**4201 - Introduction to Mathematical Statistics I U 4** [Sample Syllabus]

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 (254), 2162.xx (263), 2182H (263.01H), or 4182H (264H), or permission of instructor. Not open to students with credit for 6201 (520), 6301 (610), 6801 (620), 4201 (420), 4202 (421), or Math 4530 (530).

**4202 - Introduction to Mathematical Statistics II U 4 **[Sample Syllabus]

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 (420), Math 4530 (530), or 5530H (531H) or permission of instructor.

GE data anly course.

**4620 - Introduction to Statistical Learning U 2** [Sample Syllabus]

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.

**4690 - Undergraduate Topics in Statistics U 1-4**

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.

**4998 - Undergraduate Research in Statistics U 1-5**

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.

**5301 - Intermediate Data Analysis I U G 4** [Sample Syllabus]

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 (104) or equiv, or Math Placement Level of R, or permission of instructor. Not open to students with credit for 5301 (528 and 529). Not open to students with credit for 5301 (528 and 529) or 5302 (529 and 530).

GE data anly course.

**5302 - Intermediate Data Analysis II U G 3** [Sample Syllabus]

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:* 5299, 5301, or 529, or permission of instructor. Not open to students with credit for 530.

GE data anly course.

**5510 - Statistical Foundations of Survey Research U G 3** [Sample Syllabus]

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.

**5550 - Introductory Time Series Analysis U G 3** [Sample Syllabus]

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.

**5740 - Introduction to SAS Software U G 2** [Sample Syllabus]

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:* 5302 (530), or permission of instructor. Not open to students with credit for 574.

**5760 - Statistical Consulting Support from the SCS U G 3** [Sample Syllabus]

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.

**6030 - Teaching of Statistics G 2** [Sample Syllabus]

Introduction to the teaching of statistics; teaching strategies; communicating with students; review of topics taught in Stat 1330, 1350, and 1450; and the computing lab.*Prereq:* Grad standing in Statistics or Biostatistics. Not open to students with credit for 603. This course is graded S/U.

**6040 - Mentored Teaching Experience in Statistics G 2** [Sample Syllabus]

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.

**6060 - Early Start in Statistics G 3** [Sample Syllabus]

Selected mathematical topics (induction, integration by parts, L'Hospital's rule, and Taylor series), with applications to random variables, properties of common probability distributions, sampling distributions, and convergence in probability.*Prereq:* Grad standing in Stat or Biostat; or permission of instructor. Not open to students with credit for 602.

**6193 - Individual Studies in Foundational Graduate Topics in Statistics G 1-4**

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.

**6194 - Group Studies in Foundational Graduate Topics in Statistics G 1-5**

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.

**6201 - Mathematical Statistics G 4** [Sample Syllabus]

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 (254) or equiv, or permission of instructor. Not open to students with credit for 6301 (610) or 6801 (620).

**6301 - Probability for Statistical Inference G 3** [Sample Syllabus]

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).

**6302 - Theory of Statistical Analysis G 3** [Sample Syllabus]

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).

**6410 - Design and Analysis of Experiments G 4** [Sample Syllabus]

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).

**6450 - Applied Regression Analysis G 4** [Sample Syllabus]

Simple and multiple linear regression, diagnostics, model selection, models with categorical variables.*Prereq:* 6201 (521), or equiv, or permission of instructor. Not open to students with credit for 6950 (645).

**6510 - Survey Sampling Methods G 3** [Sample Syllabus]

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 6510 (651) or PubHBio 7225 (651). Cross-listed in PubHBio 7225.

**6520 - Applied Statistical Analysis with Missing Data G 3** [Sample Syllabus]

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.

**6530 - Introduction to Spatial Statistics G 2** [Sample Syllabus]

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.

**6540 - Applied Stochastic Processes G 3** [Sample Syllabus]

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.

**6550 - The Statistical Analysis of Time Series G 2** [Sample Syllabus]

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 (623), or 6802 (622), and 6450 (645) or 6950; or permission of instructor. Not open to students with credit for 635.

**6560 - Applied Multivariate Analysis G 3** [Sample Syllabus]

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.

**6570 - Applied Bayesian Analysis G 2** [Sample Syllabus]

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.

**6605 - Applied Survival Analysis G 3** [Sample Syllabus]

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 or 6950 or PubHBio 6211. Not open to students with credit for 6605, Biostat 605, or PubHBio 7235. Cross-listed in PubHBio 7235.

**6610 - Applied Nonparametric Statistics G 3** [Sample Syllabus]

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 (529), 6201, or 6302 (623), or equiv, or permission of instructor. Not open to students with credit for 661.

**6615 - Design and Analysis of Clinical Trials G 2** [Sample Syllabus]

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.

**6620 - Environmental Statistics G 2** [Sample Syllabus]

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.

**6625 - Statistical Analysis of Genetic Data G 3** [Sample Syllabus]

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.

**6640 - Principles of Statistical Quality Control G 3** [Sample Syllabus]

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.

**6650 - Discrete Data Analysis G 2** [Sample Syllabus]

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.

**6690 - Foundational Graduate Topics in Statistics G 1-4 ** [Sample Syllabus]

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.

**6730 - Introduction to Computational Statistics G 2** [Sample Syllabus]

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.

**6740 - Data Management and Graphics for Statistical Analyses G 3** [Sample Syllabus]

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.*Prereq:* Not open to students with credit for 674 or 675.

**6750 - Statistical Consulting and Collaboration G 2** [Sample Syllabus]

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.

**6801 - Statistical Theory I G 4** [Sample Syllabus]

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.

**6802 - Statistical Theory II G 4** [Sample Syllabus]

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.

**6860 - Foundations of the Linear Model G 2** [Sample Syllabus]

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.

**6910 - Applied Statistics I G 4** [Sample Syllabus]

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 (641).

**6950 - Applied Statistics II G 4** [Sample Syllabus]

Simple and multiple linear regression, diagnostics, model selection, the mixed model, and generalized linear models. Intended primarily for students in the PhD program in Statistics or Biostatistics.*Prereq:* 6801 (620) and 6910 (641), or permission of instructor. Not open to students with credit for 6450 (645).

**6998 - Research in Foundational Graduate Topics in Statistics G 1-5 ** [Sample Syllabus]

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.

**7201 - Theory of Probability G 3** [Sample Syllabus]

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.

**7301 - Advanced Statistical Theory I G 3** [Sample Syllabus]

Exponential families, sufficiency, Rao-Blackwell theorem, information, efficiency, maximum likelihood estimation, M-estimation, asymptotics, density estimation. Intended primarily for PhD students in Statistics or Biostatistics.*Prereq:*6802 (622), or permission of instructor. Not open to students with credit for 821.

**7302 - Advanced Statistical Theory II G 3** [Sample Syllabus]

Hypothesis testing: likelihood ratio tests, resampling and permutation based tests, sequential tests, multiple testing, asymptotic distributions of test statistics. Intended primarily for PhD students in Statistics or Biostatistics.*Prereq:*7301, or permission of instructor. Not open to students with credit for 822.

**7303 - Bayesian Analysis and Decision Theory G 3** [Sample Syllabus]

Decision theory, loss functions, subjective and objective prior distributions, posterior distribution, estimation, testing, prediction, sensitivity analysis, hierarchical modeling. Intended primarily for PhD students in Statistics or Biostatistics.*Prereq:* 7301 or 622, or permission of instructor. Not open to students with credit for 820.

**7410 - Theory of the Linear Model G 3** [Sample Syllabus]

Theory of the general linear model, estimability, power and sample size. Random effects and nested models. Analysis of covariance. Models with block variables. Generalized linear models.*Prereq:* 6802 (622), 6860, and 6950 (645) or 6910 (641); or permission of instructor. Not open to students with credit for 742.

**7430 - Generalized Linear Models G 3** [Sample Syllabus]

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 (620 and 621), 6802 (621 and 622), 6910 (641), 6950 (645), and 7410 (742); or permission of instructor. Not open to students with credit for 743.

**7470 - Advanced Longitudinal Data Analysis G 3** [Sample Syllabus]

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.

**7540 - Theory of Stochastic Processes G 3** [Sample Syllabus]

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.

**7550 - Time Series Theory and Methods G 3** [Sample Syllabus]

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.

**7560 - Multivariate Analysis G 3** [Sample Syllabus]

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.

**7605 - Advanced Regression Modeling of Time-to-Event Data G 3** [Sample Syllabus]

Advanced topics in survival analysis. Proportional hazards models, parametric regression models, length-bias and prevalent sampling, multivariate survival analysis, counting processes, recurrent events.*Prereq:* Stat 6802 (Stat 622) and Stat 6950. Not open to students with credit for PUBHBIO 706 or PUBHBIO 8235. Cross-listing in PubHBio 8235.

**7610 - Theory of Nonparametric Statistics G 3** [Sample Syllabus]

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.

**7620 - Elements of Statistical Learning G 3** [Sample Syllabus]

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.

**7630 - Nonparametric Function Estimation G 3** [Sample Syllabus]

Function estimation with emphasis on smoothing splines, flexible model building with multivariate data, reproducing kernel Hilbert space methods, additional topics in smoothing.*Prereq:* 6802 (622) and 6950 (645), or permission of instructor. Not open to students with credit for 763.

**7730 - Advanced Computational Statistics G 3** [Sample Syllabus]

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.

**7755 - Biostatistical Collaboration G 2** [Sample Syllabus]

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-listing in PubHBio 7245.

**7789 - Survey Research Practicum G 3** [Sample Syllabus]

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.

**7998 - Intermediate Graduate Research in Statistics G 1-4**

Research topics in intermediate graduate topics in Statistics.*Prereq:* Grad standing in Stat. Repeatable to a maximum of 12 cr hrs or 3 completions. This course is graded S/U.

**7999 - Masters Thesis Research in Statistics G 1-18**

Masters Thesis Research in Statistics.*Prereq:* Grad standing in Stat. Repeatable. This course is graded S/U.

**8010 - Seminar on Research Topics in Statistics G 1**

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.

**8193 - Individual Studies in Advanced Graduate Topics in Statistics G 1-5**

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.

**8194 - Group Studies in Advanced Graduate Topics in Statistics G 1-5**

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.

**8310 - Large Sample Theory G 3** [Sample Syllabus]

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.

**8410 - Capstone Applications G 3 ** [Sample Syllabus]

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.

**8450 - Stochastic Epidemic Models G 3** [Sample Syllabus]

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 PUBH-BIO 8450. Crosslisted in PUBH-BIO 8450.

**8460 - Special Topics in Design of Experiments G 3** [Sample Syllabus]

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.

**8530 - Spatial and Spatio-Temporal Statistics G 3** [Sample Syllabus]

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.

**8540 - Topics in Advanced Stochastic Processes G 3** [Sample Syllabus]

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.

**8570 - Advanced Bayesian Analysis: Modeling G 3** [Sample Syllabus]

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.

**8605 - Advanced Survival Analysis G 3** [Sample Syllabus]

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.

**8625 - Statistical Methods for Analyzing Genetic Data G 3** [Sample Syllabus]

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 (622), or permission of instructor. Not open to students with credit for 833.

**8750.01 - Research group in Statistical Learning and Data Mining G 1 **

Research group in Statistical Learning and Data Mining. Topics vary by the offering.*Prereq:* Grad standing in Statistics or Biostatistics, or permission of instructor.

**8750.02 - Research Group in Design of Physical and Computer Experiments G 1**

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.

**8750.03 - Research Group in Statistical Genetics and Bioinformatics G 1**

Research group in Genetics. Topics vary by the offering.*Prereq:* Grad standing in Statistics or Biostatistics, or permission of instructor.

**8750.04 - Research Group in Quantitive Methods in Consumer Behavior G 1**

Research group in Quantitive Methods in Consumer Behavior. Topics vary by the offering.*Prereq:* Grad standing in Statistics or Biostatistics, or permission of instructor.

**8750.05 - Research Group in Ranked Set Sampling G 1**

Research group in Ranked Set Sampling. Topics vary by the offering.*Prereq:* Grad standing in Statistics or Biostatistics, or permission of instructor.

**8750.06 - Research Group in Spatial Statistics and Environmental Statistics G 1**

Research group in Spatial Statistics and Environmental Statistics. Topics vary by the offering.*Prereq:* Grad standing in Statistics or Biostatistics, or permission of instructor./p>

**8750.08 - Research Group in Observational Data G 1**

Research group in Observational Data. Topics vary by the offering.*Prereq:* Grad standing in Statistics or Biostatistics, or permission of instructor.

**8810 - Advanced Topics in Statistics I G 1-3**

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.

**8820 - Advanced Topics in Statistics II G 1-3**

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.

**8830 - Advanced Topics in Statistics III G 1-3**

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.

**8840 - Advanced Topics in Statistics IV G 1-3**

*Prereq:* Grad standing in Statistics or Biostatistics, or permission of instructor. Repeatable to a maximum of 24 cr hrs or 8 completions.

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.

**8999 - PhD Dissertation Research in Statistics G 1-18**

PhD Dissertation research in Statistics.*Prereq:* Permission of instructor. Repeatable. This course is graded S/U.