7 October 2008

Bio752 & Bio753: Advanced Methods in Biostatistics, II & III

Teaching Approach

Lectures will emphasize concepts and interpretations, linked to techniques and examples. Readings and homework will fill in the details of lecture topics, cover additional material and provide hands (and heads!) on experience. I rely on students to be motivated, inquisitive and innovative. One consequence of this expectation is that much of the important reading isn’t explicitly assigned. Rather, the lectures and “lecture stream” provide guidance on topics to study.

Goals

Develop skills in and understanding of:

• Exploratory data analysis
• Choosing, applying, interpreting and communicating statistical analyses
• Inferential paradigms
• The (general) linear model
• The probability theory basis for statistical inference with specific reference to the Gaussian, linear model

o Assumptions and caveats

o Specifics for the Gaussian Linear Model

o Where Gaussian linear model assumptions and insights carry over to non-Gaussian and non-linear models and where they do not

o More general probability theory, see the probability notes

• The interplay between efficiency and robustness
• The relations among statistical design, analysis, reporting and subject-area science
• Identifying when a new method or adaptation is needed
• Criteria to use in evaluating a procedure
• Simulation-based methods
• Expertise in using R