About
Table of contents
About
Hierarchical models (i.e., mixed models) are widely used for analyzing data generated in agriculture, as most data has some type of hierarchical structure. This course aims to help students gain understanding and develop the intuition for the most common assumptions in mixed models. The course content is most appropriate for students who already understand the fundamental principles of applied linear models and designed experiments, and want to learn how to analyze hierarchical data using mixed models. This is a project-based course with 2 intensive weeks with classes every day, followed by a 3 month-long semester project with periodic meetings. Students are expected to start the course with a modeling project in mind. Lecture topics to be covered include but are not limited to hierarchical data structures, linear mixed models, non-linear mixed models, heteroscedastic models, and generalized linear mixed models. Potentially, topics like hierarchical Bayesian models and generalized additive models may be covered. Applications of mixed models will be focused on modeling agricultural data, including but not limited to animal science, grain science, and plant science.
Main Goal of this Course:
The main objective of this course is to learn how to design and implement hierarchical models, interpreting the results of said models and making inference. By the end of this course, students should be able to:
- Identify the data structure for a given dataset and write the statistical model that corresponds to said data structure using statistical notation.
- Distinguish the benefits and disadvantages of different modeling approaches.
- Write the Materials and Methods section in a paper (or graduate thesis) that describes the data generating process and the statistical model.
Course topics: This course will cover the basic concepts for designed experiments, including the data generation process.
Prerequisites: Instructor consent. Students are expected to have statistical background equivalent to STAT 705 and STAT 720.
Weekly schedule
- 9:00 AM
- 9:30 AM
- 10:00 AM
- 10:30 AM
- 11:00 AM
- 11:30 AM
- 12:00 PM
- 12:30 PM
- 1:00 PM
- 1:30 PM
- 2:00 PM
- 2:30 PM
- 3:00 PM
- 3:30 PM
- 4:00 PM
- 4:30 PM
- 5:00 PM
- 5:30 PM
Monday
- Lecture2:30 PM–3:45 PMDickens
Tuesday
- Lecture2:30 PM–3:45 PMDickens
Wednesday
- Lecture2:30 PM–3:45 PMDickens
Thursday
- Lecture2:30 PM–3:45 PMDickens
Friday
- Lecture2:30 PM–3:45 PMDickens
Software
Course material and examples will be provided in R. However, students may use other programming languages if appropriate.
Attendance
Attendance to lectures and in-class participation are expected. Coming late to class, leaving early, or failing to attend class will lower your grade.
Assignments
Homework assignments will be notified at least a week in advance. Incorrect assignments may be resubmitted once for full points. After that, assignments may be resubmitted for 80% of their last point worth. Late submissions will be considered for 80% of the original points.
Final project
Semester projects may deal with any topic that interests the student and is approved by the instructor. Projects are expected to identify a research problem and develop a designed experiment that is appropriate for solving that problem. Projects consist of a manuscript and a tutorial that describes the research problem, the experiment design and the treatment design. More information here.
Grading
The course will be for 3 credits, graded on an pass/fail scale: pass (100%-70%), fail (<70%). Final grade will be based on the following criteria:
| Attendance and participation 20% | Assignments 40% | Midterm Exam 20% | Final project and presentation 20% |
General Policies
Generative AI policy
Students may use generative AI tools as an assistant to complete their homework or projects but are required to understand every step of their work. Failure to justify their own work may reduce the student’s grade.
Academic Honesty
Undergraduate and graduate students, by registration, acknowledge the jurisdiction of the Honor System (www.ksu.edu/honor). The policies and procedures of the Honor System apply to all full and part-time students enrolled. A grade of XF can result from a breach of academic honesty.
Academic Accommodations for Students with Disabilities
Students with disabilities who need classroom accommodations, access to technology, or information about emergency building/campus evacuation processes should contact the Student Access Center and/or their instructor. Services are available to students with a wide range of disabilities including, but not limited to, physical disabilities, medical conditions, learning disabilities, attention deficit disorder, depression, and anxiety. If you are a student enrolled in campus/online courses through the Manhattan or Olathe campuses, contact the Student Access Center at accesscenter@k-state.edu, 785-532-6441.
Expectations for Classroom Conduct
All student activities in the University, including this course, are governed by the Student Judicial Conduct Code as outlined in the Student Government Association By Laws, Article VI, Section 3, number 2. Students that engage in behavior that disrupts the learning environment may be asked to leave the class.
Copyright Notification
During this course, students are prohibited from selling notes to or being paid for taking notes by any person or commercial firm, or posting lecture notes on any websites without the express written permission of the professor teaching this course.