This course introduces statistical methods for advanced data analysis, especially regression-based methods. Based on the characteristics of data and analysis purpose, students will learn how to find appropriate statistical models, how to fit the data and interpret the results. Through the course project, student will apply the methods to real data. The course will cover the following topics:
· Linear model and linear mixed model
· Generalized linear model
· Shrinkage method and variable selection
· Graphical methods and causal Inference
Linear regression and GLM materials are from the following book. PDF versions of books are available online. But the course will mostly follow course slides. So it is not required to buy the books.
· Julian J Faraway, Linear models with R, 2nd Edition. CRC Press (Chapman & Hall).
· Dobson, AJ., Barnett, A.G. An Introduction to Generalized Linear Models, 3rd Edition. CRC Press (Chapman & Hall).
01. Course introduction: Review of key distributions and matrix algebra
02. Linear regression 1
03. Linear regression 2
04. Linear regression 3
05. Mixed effect model
06. Shrinkage/Penalized methods
07. GLM Introduction
08. GLM Estimation and Midterm
09. GLM Inference
10. Logistic regression
11. Logistic regression
12. Multinomial regression
13. Poisson regression
14. Graphical Model and Causal Inference
15. Final project presentation
· Attendance: 5%
· Task: 40%
· Midterm: 25%
· Final: 30%
Students are expected to have background in basic statistics.