Below are the courses offered by the Department of Biostatistics.
|Fall 2013 Biostatistics Courses|
|PHP2507||Biostats & Applied Data Analysis I||Th: 1:00-2:30
|The objective of the year long, two-course sequence is for students to develop the knowledge, skills and perspectives necessary to analyze data in order to answer a public health questions. The year long sequence will focus on statistical principles as well as the applied skills necessary to answer public health questions using data, including: data acquisition, data analysis, data interpretation and the presentation of results. Through lectures, labs and small group discussions, this fall semester course will focus on identifying public health data sets, refining research questions, univariate and bivariate analyses and presentation of initial results. Prerequisite: understanding of basic math concepts and terms; basic functional knowledge of Stata. Enrollment limited to 50 MPH, CTR, and BSSI students. Instructor permission required.
|PHP2510||T Th: 9:00-10:20||Cici Bauer||Intensive first course in biostatistical methodology, focusing on problems arising in public health, life sciences, and biomedical disciplines. Summarizing and representing data; basic probability; fundamentals of inference; hypothesis testing; likelihood methods. Inference for means and proportions; linear regression and analysis of variance; basics of experimental design; nonparametrics; logistic regression. Open to advanced undergraduates with permission from the instructor.
|PHP2520||T Th: 2:30-3:50||Joseph Hogan||First of two courses that provide a comprehensive introduction to the theory of modern statistical inference. PHP 2520 presents a survey of fundamental ideas and methods, including sufficiency, likelihood based inference, hypothesis testing, asymptotic theory, and Bayesian inference. Measure theory not required. Open to advanced undergraduates with permission from the instructor.
|PHP2530||MW: 9:00-10:20||Roee Gutman||Surveys the state of the art in Bayesian methods and their applications. Discussion of the fundamentals followed by more advanced topics including hierarchical models, Markov Chain Monte Carlo, and other methods for sampling from the posterior distribution, robustness, and sensitivity analysis, and approaches to model selection and diagnostics. Features nontrivial applications of Bayesian methods from diverse scientific fields, with emphasis on biomedical research. Prerequisites: APMA 1650, PHP 2510, PHP 2511, or equivalent. Open to advanced undergraduates with permission from the instructor.
|Xi (Rossi) Luo||Generalized linear models provide a unifying framework for regression. Important examples include linear regression, log-linear models, and logistic regression. GLMs for continuous, binary, ordinal, nominal, and count data. Topics include model parameterization, parametric and semiparametric estimation, and model diagnostics. Methods for incomplete data are introduced. Computing with modern software is emphasized. Prerequisites: APMA 1650 or PHP 2520. Open to advanced undergraduates with permission from the instructor.
|PHP2690D||MW: 10:30-11:50||Chrisopher Schmid||Designed for graduate and advanced undergraduate students who will be analyzing data and want to develop a practical hands-on toolkit. Topics including data collection and management, exploratory data analysis, fitting and checking models, simulation, handling missing data and presentation of results will be developed through a series of case studies based on different types of data requiring a variety of statistical methods. Statistical programming techniques including functions, graphs and tables will be emphasized. Students should have familiarity with basic concepts of statistics through regression. Permission of instructor required.
To view all public health courses, please visit the Brown's Course site.