Courses Taught by Trivellore Raghunathan

BIOSTAT501: Introduction to Biostatistics

  • Graduate level
  • Residential and Online MPH and Online MS
  • This is a first year course for Online students
  • Fall term(s) for residential students; Winter term(s) term for online MPH students; Spring-Summer term(s) term for online MS students.
  • 3 Credit Hour(s) for residential students; 2 Credit Hour(s) for online MPH students for residential students; 3 Credit Hour(s) for online MS students
  • Instructor(s): Raghunathan, Trivellore Braun, Thomas (Residential); Braun, Thomas (Online MPH); Braun, Thomas (Online MS);
  • Prerequisites: SPH MPH or permission of instructor
  • Description: Statistical methods and principles necessary for understanding and interpreting data used in public health and policy evaluation and formation. Topics include descriptive statistics, graphical data summary, sampling, statistical comparison of groups, correlation, and regression. Students will learn via lecture, group discussions, critical reading of published research, and analysis of data.
  • This course required for the school-wide core curriculum
  • Residential Syllabus for BIOSTAT501
Concentration Competencies that BIOSTAT501 Allows Assessment On
Department Program Degree Competency Specific course(s) that allow assessment
Population and Health Sciences MPH Compare population health indicators across subpopulations, time, and data sources PUBHLTH515, BIOSTAT592, EPID590, EPID592, EPID643, BIOSTAT595, BIOSTAT501
Population and Health Sciences MPH Estimate population health indicators from high quality data resources from diverse sources PUBHLTH515, EPID643, NUTR590, BIOSTAT592, BIOSTAT501

BIOSTAT594: Applied Generalized Linear Models

  • Graduate level
  • Both Online MPH and Online MS
  • This is a first year course for Online students
  • Fall term(s) term for online MPH students; Fall term(s) term for online MS students.
  • 3 Credit Hour(s) for online MPH students for residential students; 3 Credit Hour(s) for online MS students
  • Instructor(s): Wu, Zhenke (Online MPH); Wu, Zhenke (Online MS);
  • Prerequisites: BIOSTAT501, BIOSTAT591, BIOSTAT592
  • Description: This course introduces public health Master's students to generalized linear models to analyze binary, discrete, ordinal, count, survival outcomes.The primary emphasis will be interpretation, inference and hands-on data analyses. We will use R to analyze public health datasets, evaluate regression assumptions, and assess model fit.
  • Learning Objectives: 1. Understand the context where non-continuous outcome data are generated, identify the most relevant aspects of these data that require modeling and formulate a scientific question in terms of one or a few model parameters 2. To develop the ability to use R to analyze public health data using GLM 3. Interpret results of data analysis for public health research, policy or practice
  • This course is cross-listed with .