Syllabus.

(see also the course webpage))

Regression and Data Analysis

STATS 513-WIN 2019

WF 8:30-10:00am 1449MH

Instructor: Xianshi (Sean) Yu

​ Email: xsyu@umich.edu

​ Office hours: Tue 1:30-3:30pm, 256 West Hall

GSI: Brad Zhao

​ Email: rfzhao@umich.edu

​ Office hours: Thu 5:30-7:00pm, Science Learning Center

Course Outline:

• Linear models (basic): definition, fitting, interpretation of results
• Linear models (advanced): variable selection, transformations, diagnostics, outliers and influential observations, model selection, shrinkage methods
• a complete example with real data

logistic regression is selective, depending on the progress of the course

Text Books:

Julian Faraway (2014) Linear Models with R, 2nd edition. Julian Faraway (2016) Extending the Linear Models with R, 2nd edition. (Supplementary)

Computing:

The software R will be used for this course, which is free for both Windows and Mac. Instructions about the basic implementations of R will be included in the lectures.

Prerequisite: Knowledge of matrix algebra, introductory probability, and mathematical statistics.

Grading: Homework (30%), Midterm (30%), Final (40%)

Homework: R is the required software for completing the homework. Late homeworks are not accepted. If you are unable to attend class on the day a homework is due, e-mail it to the grader (the GSI) before the due date. The worst homework score will be dropped.

Exams:

• Midterm: 03/20/2019 Wed 8:30-10:00am, in class.
• Final: 04/25/2019 Thu 4:00-6:00pm, 1449MH (not cumulative)