LECTURE: MW 10:00-11:20 a.m., Olson 159 LAB: MW 11:30-11:50 a.m., Olson 159 INSTRUCTOR: Prof.
Jiming Jiang, 4228 MSB, jimjiang@ucdavis.edu;
Office hours: R 11:00 a.m.- 12:00 p.m. or by appt.
TA: Ms. Yuan-Jung Cynthia Juang,
ycjuang@ucdavis.edu; Office hours: M 12:10-1:00 p.m., 1117 MSB.
Reference Books:1. Linear and Generalized Linear Mixed Models
:nd Their Applications, Second Edition by J. Jiang and T. Nguyen (Springer 2021)
2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
by T. Hastie, R. Tibshirani, and J. Friedman
(Springer 2009)
3. Linear Models with R, Second Edition by J. Faraway (CRC Press 2015) REQUIREMENTS:
HOMEWORK: Homeworks will be assigned during the lectures, and due
in the Lab each Wed. (starting the second week), unless an
extension is given. The lowest homework grade will be dropped in
calculating the overall homework score. No late homework.
MIDTERM: There will be an in-class midterm
exam. Date: Wed., Feb. 4 (2026), 10:00 - 11:50 a.m. (same location as the Lecture/Lab).
CLASS PROJECTS: A number of class projects will be assigned. The
class projects are expected to be completed individually.
FINAL PROJECT/PRESENTATION: There will be a final project which
also includes a presentation. The final project is expected to
be completed by groups of 2-4 members with one project report and one
presentation per group.
The project involves identifying a problem associated with a data set
(e.g., from publicly
available sources), proposing a statistical model based on knowledge
learnt from the class, carrying out appropriate statistical analyses
using software learnt from the class, interpreting and summarizing
the results. The final project is assigned at the begining of the
quarter and runs though the quarter. Project presentation:
TBA. Project due date: TBA
GRADES:
FINAL PROJECT 30%, CLASS PROJECTS 30%, MIDTERM 20%, HOMEWORK 20%