Lectures: MW 11:15am – 12:30pm, Virtual. Syllabus
Instructor: Yufeng Liu
Office Hours: MW 12:30-1:30pm
TA: Daiqi Gao (Ph.D Student in Statistics) Email: dqgao@live.unc.edu Office Hours: Tuesdays 2-3pm; Thursdays 3-4pm (Note: No TA hour office on Jan. 28th; the alternative hour office is on 3-4pm Jan. 27th)
Enrollment Questions: Please contact Ms. Christine Keat for questions and assistance regarding the enrollment for this class.
Textbooks:
The course will primarily be based on lecture notes. In terms of textbook, we will use the following book by Alan Agresti as the main reference textbook.
- Alan Agresti (2015) Foundations of Linear and Generalized Linear Models, Wiley.
Some other reference books for optional further reading if interested.
- McCullagh and Nelder (1989)- Generalized Linear Models, Chapman & Hall
- Jiang (2007)- Linear and Generalized Linear Mixed Models and Their Applications. Springer.
- Faraway (2006) – Extending the Linear Model with R, Chapman & Hall/CRC.
Statistical Software:
We will use R for this course. R is free so you can easily use it anytime and anywhere. R can be downloaded from the R website. Rstudio is a recommended interface for the R software. It is also free, and it runs on Windows, Mac, and Linux operating systems. R Markdown can be used to produce high quality documents and reports.
Reference: W. N. Venables, D. M. Smith, and the R Core Team. 2020. An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics (version 4.0.3).
Evaluation & Grading:
There will be homework assignments through the semester on both the theoretical and computational aspects of the course.
The course grade will be given based on class participation, homework grades, project, and exams (midterm and final exams).
The distribution of the grade is as follows:
• Project 15%;
• Final 30%
Homework Policy:
Homework assignments will be posted on the course web page. Each homework assignment will be graded: late/missed homework assignments without permission will receive a grade of zero. Assignments can be submitted in Sakai on the day they are due, so please be prepared to turn in your homework at that time.
Honor Code:
Students are expected to adhere to the UNC honor code at all times. Violations of the honor code will be prosecuted.
ANNOUNCEMENTS, ASSIGNMENTS & LECTURES:
Lectures | Date | Tentative Plan | Remark |
1 | Jan 20 W | Introduction & Overview Reading: Agresti Ch1-3 Homework 1 (updated) | Review Slides |
2 | Jan 25 M | One-way ANOVA Review Reading: Aregsti Ch1-3 Homework 2 (updated) | ANOVA/ANCOVA Slides |
3 | Jan 27 W | Two-way ANOVA Review Reading: Aregsti Ch1-3 | GLM Slides |
4 | Feb 1 M | ANCOVA Reading: Aregsti Ch1-3 | Homework 1 Due |
5 | Feb 3 W | GLM Basics Reading: Aregsti Ch 4 | |
6 | Feb 8 M | GLM Basics Reading: Aregsti Ch 4 Homework 3 | |
7 | Feb 10 W | GLM Basics Reading: Aregsti Ch 4 | |
Feb 12 F | Homework 2 Due (New due date) | ||
Feb 15 M | No Class | Binary Data Slides | |
8 | Feb 17 W | GLM Basics Reading: Aregsti Ch 4 | |
9 | Feb 22 M | Binary Data Reading: Aregsti Ch 5 | |
10 | Feb 24 W | Binary Data Reading: Aregsti Ch 5 | |
Feb 26 F | Homework 4 | Homework 3 Due (New due date) | |
11 | Mar 1 M | Binary Data Reading: Aregsti Ch 5 | |
12 | Mar 3 W | Binary Data Reading: Aregsti Ch 5 | Categorical Data Slides |
13 | Mar 8 M | Binary Data Reading: Aregsti Ch 5 | Sample Midterm |
14 | Mar 10 W | Binary Data Reading: Aregsti Ch 5 | |
Mar 12 F | Homework 4 Due | ||
15 | Mar 15 M | Review/Categorical Data Reading: Aregsti Ch 6 | |
16 | Mar 17 W | Midterm Homework 5 | |
17 | Mar 22 M | Go over Midterm/Categorical Data Reading: Aregsti Ch 6 | Count Data Slides |
18 | Mar 24 W | Categorical Data Reading: Aregsti Ch 6 | |
19 | Mar 29 M | Count Data Reading: Aregsti Ch 7 | |
20 | Mar 31 W | Count Data Reading: Aregsti Ch 7 | |
Apr 1 Th | Homework 6 | Homework 5 Due | |
Apr 5 M | No class | Linear Mixed Model Slides | |
21 | Apr 7 W | Count Data Reading: Aregsti Ch 7 | Project Proposal Due |
22 | Apr 12 M | Count Data/Linear Mixed Models Reading: Aregsti Ch 7, 9 | |
23 | Apr 14 W | Linear Mixed Models Reading: Aregsti Ch 9 | |
Apr 16 F | Homework 7 | Homework 6 Due | |
24 | Apr 19 M | Linear Mixed Models Reading: Aregsti Ch 9 | |
25 | Apr 21 W | Linear Mixed Models Reading: Aregsti Ch 9 | |
26 | Apr 26 M | Linear Mixed Models Reading: Aregsti Ch 9 | |
27 | Apr 28 W | Review/Project Presentation (12min +1min Q&A): 1. Ackerman; 2. Allen; 3. Chen | |
Apr 30 F | Homework 7 Due | ||
28 | May 3 M | Project Presentation: 4. Kovach/Lavond/Mitchell; 5. Cheng; 6. Ferer; 7. He; 8. Hoellerbauer | |
29 | May 5 W | Project Presentation: 9. Li; 10. Suo; 11. White; 12. Yi; 13. Zhang | |
May 6 Th | Final report due | ||
Office hours before the Final Yufeng: Monday May 10 11am-noon; Wednesday May 12 1-2pm; Friday May 14 10-11am
TA: Tuesday May 11 2-3pm; Thursday May 13 3-4pm Final Exam Friday May 14 12pm |