19 Syllabus

Instructor: Trevor Hefley
Email:
Office: 002 Dickens Hall
Lecture: Monday through Friday, 11:30 am - 12:30 pm (2100 College of Business Building) In-person Office Hours: Everyday after the lecture, 12:30 pm - 1:15 pm (2100 College of Business Building) Zoom Office Hours: By request (send me an email) and use https://ksu.zoom.us/j/97489320573

Introduction: This course is designed for advanced undergraduates in quantitative fields of study (e.g., statistics, data science, mathematics) and graduate students in any field of study. The course focuses on statistical modeling, prediction/forecasting, and inference, which will be understood using regression and analysis of variance.

Prerequisites: One previous statistics course.

Recommended textbooks:

  • Note that none of the books listed are required for this course
  • Faraway, J. (2025) Linear Models with R, Third Edition. Chapman and Hall/CRC. Amazon
  • Xie Y., Allaire J., Grolemund G. (2019) R Markdown: the definitive guide. CRC Press. bookdown
  • Fieller, N. (2015) Basics of Matrix Algebra for Statistics with R. Chapman and Hall/CRC.

Recommended Software:

Tentative topics: Our objective is to cover the majority of the material in Linear Models with R. This text will be supplemented with additional material that helps students build their statistical modeling and programming skills. During the semester, we will cover the basics such as regression and ANOVA modeling, parameter estimation, model checking, inference, and prediction. We may also cover modern topics such as regularization, random effects, generalized linear models, machine learning approaches, and Bayesian regression and ANOVA. Additionally, we will develop the skills necessary to produce documents that adhere to the standards of reproducible research.

Reproducibility requirement: I expect that all activities, projects and other assignments that involve reporting of the results obtained from computer programs will be reproducible. In the most basic sense, this is equivalent to “showing your work” as you would for assignments with analytical components. I will introduce tools during the lectures that will show you how to do this.

Grading: The course will be for three credits, graded on an A-F scale. A (>90%), B (90%-80%), C (80%-70%), D (70%-60%), and F (<60%). Your final grade will be based on the contributions listed below.

  • Daily journal (40%): For each of the 38 scheduled meetings, a journal entry will be turned in by 24 hours following class (i.e., turned in by 11:30 am on Tuesday for a Monday class). These journal entries will be graded on a pass/fail basis, with 1 point awarded for a pass and zero points awarded for a fail. Each journal entry will contain ~ ½ - 1 page of written responses to the three points below. These responses will be uploaded as a pdf file to Canvas. If you do not attend class you are not eligible to complete the journal assignment. In other words, do not upload a journal for a class that you did not attend.
    • Write a paragraph about something new you learned in class within the past 24 hours.
    • Write a paragraph about something you are struggling to understand that was covered in class within the past 24 hours.
    • Is there anything else you would like me to be aware of?
  • Assignments (20%): There will be 4-6 assignments given throughout the summer.
  • Final project (40%): A final project will be assigned. Please plan to meet with me during office hours during the summer to discuss your project.

General Policies: Class attendance is expected. Being on time to lecture is expected. If you will miss a lecture or be late, you are required to send me an email before the lecture that you will be missed or be late too. Late journal entries, assignments, and project components will not be accepted and will receive a grade of zero. If you do not attend class you are not eligible to complete the journal assignment. In other words, do not upload a journal for a class that you did not attend. It will be considered an act of academic dishonesty if you attempt to complete the journal assignment for a class you did not attend.

Artificial Intelligence Policies: Use of AI tools is encourage, but with the caveat that you must tell me what tool you used, how you used it, and for what part of the assignment. Not adhering to this policy will result in a grade of zero and potential be considered a act of academic dishonesty.

Academic Honesty: Kansas State University has an Honor and Integrity System based on personal integrity, which is presumed to be sufficient assurance that, in academic matters, one’s work is performed honestly and without unauthorized assistance. Undergraduate and graduate students, by registration, acknowledge the jurisdiction of the Honor and Integrity System. The policies and procedures of the Honor and Integrity System apply to all full and part-time students enrolled in undergraduate and graduate courses on-campus, off-campus, and via distance learning. A component vital to the Honor and Integrity System is the inclusion of the Honor Pledge, which applies to all assignments, examinations, or other coursework undertaken by students. The Honor Pledge is implied, whether or not it is stated: “On my honor, as a student, I have neither given nor received unauthorized aid on this academic work.” A grade of XF can result from a breach of academic honesty. The F indicates failure in the course; the X indicates the reason is an Honor Pledge violation.

Academic Accommodations for Students with Disabilities: At K-State it is important that every student has access to course content and the means to demonstrate course mastery. Students with disabilities may benefit from services including accommodations provided by the Student Access Center. Disabilities can include physical, learning, executive functions, and mental health. You may register at the Student Access Center or to learn more contact: Manhattan/Olathe/Global Campus – Student Access Center, , 785-532-6441.

Students already registered with the Student Access Center please request your Letters of Accommodation early in the semester to provide adequate time to arrange your approved academic accommodations. Once SAC approves your Letter of Accommodation it will be e-mailed to you, and your instructor(s) for this course. Please follow up with your instructor to discuss how best to implement the approved accommodations.

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 Governing Association By Laws, Article V, Section 3, number 2. Students who engage in behavior that disrupts the learning environment may be asked to leave the class.

Mutual Respect and Inclusion in K-State Teaching and Learning Spaces: At K-State, faculty and staff are committed to creating and maintaining an inclusive and supportive learning environment for students from diverse backgrounds and perspectives. K-State courses, labs, and other virtual and physical learning spaces promote equitable opportunity to learn, participate, contribute, and succeed, regardless of age, race, color, ethnicity, nationality, genetic information, ancestry, disability, socioeconomic status, military or veteran status, immigration status, Indigenous identity, gender identity, gender expression, sexuality, religion, culture, as well as other social identities.

Faculty and staff are committed to promoting equity and believe the success of an inclusive learning environment relies on the participation, support, and understanding of all students. Students are encouraged to share their views and lived experiences as they relate to the course or their course experience, while recognizing they are doing so in a learning environment in which all are expected to engage with respect to honor the rights, safety, and dignity of others in keeping with the K-State Principles of Community.

If you feel uncomfortable because of comments or behavior encountered in this class, you may bring it to the attention of your instructor, advisors, and/or mentors. If you have questions about how to proceed with a confidential process to resolve concerns, please contact the Student Ombudsperson Office. Violations of the student code of conduct can be reported using the Code of Conduct Reporting Form. You can also report discrimination, harassment or sexual harassment, if needed.

Discrimination, Harassment, and Sexual Harassment: Kansas State University is committed to maintaining academic, housing, and work environments that are free of discrimination, harassment, and sexual harassment. Instructors support the University’s commitment by creating a safe learning environment during this course, free of conduct that would interfere with your academic opportunities. Instructors also have a duty to report any behavior they become aware of that potentially violates the University’s policy prohibiting discrimination, harassment, and sexual harassment, as outlined by PPM 3010.

If a student is subjected to discrimination, harassment, or sexual harassment, they are encouraged to make a non-confidential report to the University’s Office for Institutional Equity (OIE) using the online reporting form. Incident disclosure is not required to receive resources at K-State. Reports that include domestic and dating violence, sexual assault, or stalking, should be considered for reporting by the complainant to the Kansas State University Police Department or the Riley County Police Department. Reports made to law enforcement are separate from reports made to OIE. A complainant can choose to report to one or both entities. Confidential support and advocacy can be found with the K-State Center for Advocacy, Response, and Education (CARE). Confidential mental health services can be found with Lafene Counseling and Psychological Services (CAPS). Academic support can be found with the Office of Student Life (OSL). OSL is a non-confidential resource. OIE also provides a comprehensive list of resources on their website. If you have questions about non-confidential and confidential resources, please contact OIE at or (785) 532–6220.

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.