Introduction to Biostatistics


Teaches the principles of biostatistical analysis

Course Director: Jill Waalen
Term: Winter
Credits: 3.0

The focus of this course will be on basic concepts, principles, and practices in statistical analyses. Fundamentals of probability theory, distribution theory, and parameter estimation techniques will be discussed as the bases for understanding the application of statistical tests. The majority of the course will be dedicated to the application of statistical tests commonly encountered in scientific fields relevant to the students. This will include hypothesis testing and model building strategies, including assumption checking (e.g., normality, outliers, scale), graphical methods (e.g., scatter plots, box plots), and model diagnostics (e.g., serial correlation, normality). The lectures are given in sequence with each lecture building on the material covered in prior lectures.

The free on-line statistical package R will be used as an integral part of the lectures and coursework. No prior experience with R is required. Basic R programming will be learned as a practical complement to statistical concepts as they are covered. R code required to perform simulations and other procedures that demonstrate and apply these concepts will be provided for the students and discussed during regular class sessions. Optional Friday workshops will allow students to get individualized help with using the program. This course originates as an education and training course offered from the Scripps Research Translational Institute, which is dedicated to improving medicine and human health via biomedical discoveries. SRTI is a member of the NIH-funded Clinical and Translational Science Award (CTSA) Consortium.

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