Basic Course Info
Roberta De Vito
Assistant Professor of Biostatistics
Email: firstname.lastname@example.org, Office Hours: Thursday 16:00-17:00, on line
Email: zhe email@example.com, Office Hours: Tuesday 7-9 PM, on line
Email: firstname.lastname@example.org, Office Hours: Friday 3.30-5 PM, on line
Lei Shiqi Email: shiqi email@example.com, Office Hours: Monday 7-9 PM, on line
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 regression-based approaches to data-analysis.
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.
For full details look the canvas site.
- James G, Witten D, Hastie T, Tibshirani R (2013). Introduction to Statistical Learning, with Applications in R. Springer. http://www-bcf.usc.edu/?gareth/ISL/index.html