Lawrence Joseph
Bayesian statistics

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Basic information
Instructor Lawrence Joseph
Credits 4
Course Objectives and Topics CoveredUnivariate and multivariate statistical techniques for continuous and dichotomous outcomes. Focus is on multiple linear and logistic regression models. Additional topics will include adjusting for missing data, measurement error, and hierarchical (random effects) models. All material will be taught from both Bayesian and frequentist viewpoints. R and WinBUGS software will be used throughout the course.
Place and TimeJanuary 7 to April 10, 2019. Time: Mondays and Wednesdays, 11:30 AM to 1:30 PM. Room: McIntyre Medical Building, Room 1034.
AssessmentAssignments = 5 * 4% (each) = 20%, Midterm Exam = 30%, Final Exam = 50%.
Some suggested Textbooks (none required)Michael H. Kutner, John Neter, Christopher J. Nachtsheim, William Li. Applied Linear Statistical Models, 4th Edition, McGraw-Hill, 2004.
David W. Hosmer and Stanley Lemeshow, Applied Logistic Regression, 2nd (or more recent) Edition Wiley, 2000.
George G. Woodworth. Biostatistics: A Bayesian Introduction. Wiley, New York, 2004.
Andrew Gelman, John Carlin, Hal Stern and Donald. Rubin, Bayesian Data Analysis, 2nd (or more recent) Edition, Chapman and Hall, 2003.
PrerequisitesPrevious courses in differential and integral calculus, and EPIB 607 or equivalent.