Basic Course Info
Instructors:
Roberta De Vito
Assistant Professor of Biostatistics
Email: roberta_devito@brown.edu, Office Hours: Fridays 12:301:30, DSI 323
Teaching Assistants:
Kun Meng
Email: kun_meng@brown.edu, Office Hours: Mondays 24, DSI 329
Amy Liu
Email: amy_liu1@brown.edu
Course Overview
This course provides a modern introduction to inferential methods for regression analysis and statistical learning, with an emphasis on application in practical settings in the context of learning relationships from observed data. Topics will include basics of linear regression, variable selection and dimension reduction, and approaches to nonlinear regression. Extensions to other data structures such as longitudinal data and the fundamentals of causal inference will also be introduced. At the end of the course, students should be able to do the following:

Describe the statistical underpinnings of regressionbased approaches to dataanalysis.

Use R to implement basic and advanced regression analysis on real data.

Develop written explanations of data analyses used to answer scientific questions in context.

Provide a critical appraisal of common statistical analyses, including choice of method and assumptions underlying the method.
Readings
 James G, Witten D, Hastie T, Tibshirani R (2013). Introduction to Statistical Learning, with Applications in R. Springer. http://wwwbcf.usc.edu/?gareth/ISL/index.html