# 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.
• 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.
• 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.
• 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.
• 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.
• 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).
• 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.

## 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.
• 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.
• 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.
• 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).