Education / Graduate Education / PhD Programs / Statistics / Curriculum / Syllabi Sample Syllabi From Previous Years BST 401 Probability Theory Semester: Fall Description: Probability spaces; random variables; independence; distributions; expectation; characteristic functions and inversion theorems; convergence; laws of large numbers; central limit theorem. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. BST 401 Syllabus BST 411 Statistical Inference Semester: Fall Description: Probability distributions, transformations and sampling distributions; statistical models; estimation, hypothesis testing, and confidence intervals for parametric models; introduction to large-sample methods. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. BST 411 Syllabus BST 413 Bayesian Inference Semester: Spring Description: Posterior distributions for single and multiple parameter models under conjugacy; hierarchical models; noninformative and informative prior distributions; modern computational techniques, including Markov chain Monte Carlo; model checking; posterior predictive checks; sensitivity analysis. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. BST 413 Syllabus BST 426 Linear Models Semester: Spring Description: Theory of least-squares; point estimation in the general linear model; projection operators, estimable functions and generalized inverses; tests of general linear hypotheses; power; confidence intervals and ellipsoids; simultaneous inference; linear and polynomial regression; analysis of variance and analysis of covariance models; fixed, random, and mixed effects; correlation; prediction. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. BST 426 Syllabus BST 430 Introduction to Statistical Computing Semester: Fall Description: Basic/intermediate R programming; statistical analysis in R; visualization in R; introduction to SAS programming; statistical analysis in SAS; reproducible research and collaborative coding; command line tools and BlueHive. Topics in statistical analysis provide working examples. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. BST 430 Syllabus BST 432 High Dimensional Data Analysis Semester: Fall Description: Application of statistical theory to the analysis of high throughput data; introduction to Bioconductor; molecular profiles (mRNA, cDNA, microRNA, proteomics); platforms (Affymetrix and other microarrays, PCR, RNA seq); quality control (quality assessment, batch-effects); exploratory methods (graphical methods, clustering, principal component analysis and other dimension reduction techniques); differential expression and multiple hypothesis testing; classification (feature selection, multivariate methods, machine learning, cross-validation). Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. BST 432 Syllabus BST 434 Genomic Data Analysis Semester: Spring Description: Introduction to techniques used in modern genomic experimentation and the corresponding statistical methods and software available to visualize, analyze, and interpret these data. Specific topics include mRNA/microRNA expression, protein abundance, protein-DNA binding, copy number variants, single nucleotide variants, DNA methylation, and microbial abundance. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. BST 463 Introduction to Biostatistics Semester: Fall Description: Introduction to statistical techniques with emphasis on applications in the health sciences. Summarizing and displaying data; introduction to probability; Bayes' theorem and its application in diagnostic testing; binomial, Poisson, and normal distributions; sampling distributions; estimation, confidence intervals, and hypothesis testing involving means and proportions; simple correlation and regression; contingency tables; use of statistical software. Can students outside the department’s program(s) take it? Yes BST 463 Syllabus BST 465 Design of Clinical Trials Semester: Spring Description: Introduction to the principles of clinical trials; clinical trial protocols; overview of the drug development process; hypotheses/objectives; specification of response variables; defining the study population; randomization; blinding; ethical issues; factorial designs; crossover designs; equivalence trials; trial monitoring and interim analyses; sample size and power; issues in data analysis and reporting; evaluating clinical trial reports. Can students outside the department’s program(s) take it? Yes BST 465 Syllabus BST 467 Applied Statistics in the Biomedical Sciences Semester: Spring Description: Introduction to statistical techniques with emphasis on applications in the biomedical sciences. Introduction to probability and probability distributions; sampling distributions; estimation, confidence intervals and hypothesis testing in small and large samples; analysis of categorical data; analysis of variance; correlation and linear and nonlinear regression analysis; use of statistical software; illustrations using published articles in the biomedical sciences. Can students outside the department’s program(s) take it? Yes BST 467 Syllabus BST 487 Seminar in Statistical Literature Semester: Spring and Fall Description: Provides an introduction to the process of searching the statistical literature, opportunities to acquire knowledge of a focused area of statistical research, experience in organizing, preparing, and delivering oral presentations, and an introduction to the research interests of members of the faculty. Can students outside the department’s program(s) take it? No BST 511 Topics in Statistical Inference I Semester: Fall Description: Advanced topics in statistical inference and/or decision theory. Topics may change each year. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. Fall 2018 Topic: Functional Data Analysis BST 512 Topics in Statistical Inference II Semester: Spring Description: Advanced topics in statistical inference and/or decision theory. Topics may change each year. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. Spring 2019 Topic: Semiparametric Inference BST 514 Survival Analysis Semester: Fall Description: Parametric, nonparametric, and semiparametric methods for the analysis of survival data. right censoring; Kaplan-Meier curves; log-rank and weighted log-rank tests; survival distributions; accelerated life and proportional hazards regression models; time-dependent covariates; partial likelihood; models for competing risks and multiple events. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. BST 514 Syllabus BST 531 Nonparametric Inference Semester: Fall Description: Nonparametric estimation and inference for one-sample location and paired data, two-sample location and/or dispersion, one- and two-way layouts with and without order restrictions, tests of independence, and regression; exact and large-sample results for some commonly used procedures, including the sign test and the sample median, the Mann-Whitney-Wilcoxon test and the Hodges-Lehmann location measure, and some generalizations to more complex data structures; density estimation; nonparametric regression; generalized additive models (GAM); cross-validation; bandwidth selection; exact and asymptotic bias, variance, and mean squared error (MSE). Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. BST 531 Syllabus BST 570 Topics in Biostatistics Semester: Spring Description: Advanced biostatistical techniques. Topics may change each year. Can students outside the department’s program(s) take it? Please contact instructor to discuss your background. Spring 2019 Topic: Analysis of Marked Endpoints