STOR665: Applied Statistics II

Lectures: MW 11:15am – 12:30pm, Virtual. Syllabus

InstructorYufeng 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.

Some other reference books for optional further reading if interested.

Statistical Software:

 R

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 websiteRstudio 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:

• Homework 30%;
• Midterm 25%;
• 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