Harvard Biostatistics Multiple Lecture Courses

Course Title: Methods I

Course ID: BST232 (Full Term Course)

Topics to be covered:
This course focuses on association analyses for continuous and binary data. Topics in the first half of the course include ANOVA and extensions, permutation inference, and linear regression. Linear regression topics include model fitting, modern methods for inference, model checking, and prediction. The second half of the course on binary data will include a review of sampling plans, analysis of contingency tables, large sample and exact methods for constructing confidence intervals and hypothesis tests, measures of association, logistic regression and extensions.

Prerequisites: Ask the course instructor

Prior math/stat knowledge: Students should be proficient in matrix algebra and intermediate calculus, including partial differentiation and function maximization/minimization.

Computing: The primary statistical packages for the course will be both R and SAS. However, students are welcome to use STATA if they prefer.