Basic Course Info

Roberta De Vito
Assistant Professor of Biostatistics
Email:, Office Hours: Fridays 12:30-1:30, DSI 323

Teaching Assistants:
Kun Meng
Email:, Office Hours: Mondays 2-4, DSI 329

Amy Liu

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:

  1. Describe the statistical underpinnings of regression-based approaches to data-analysis.

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

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

  4. Provide a critical appraisal of common statistical analyses, including choice of method and assumptions underlying the method.


  1. James G, Witten D, Hastie T, Tibshirani R (2013). Introduction to Statistical Learning, with Applications in R. Springer.

Full Syllabus