Syllabus
MANE6313: Design of Experiments
Syllabus
Spring 2026
Subject to any new Texas legislative mandate changes.
Course Information¶
Meeting Days, Time, Location: This class is offered in the online asynchronous mode and there are no manditory meetings.
Optional Weekly Update: I will offer an optional (voluntary) weekly meeting Thursday from 7:00 - 7:30 pm. This meeting will be recorded and shared with the class.
Course Modality: Online Asynchronous Courses (OASYNC)
Instructor Information¶
Instructor Name: Dr. Douglas Timmer
UTRGV E-mail: douglas.timmer@utrgv.edu
Office Phone: 956-665-2608
Office Location: ENGR 3.258
Office Hours: Monday - Thursday 9:30 - 10:30 AM or by appointment
Welcome and Teaching Philosophy¶
Welcome to MANE 6313 - Design of Experiments. This course will provide you the knowledge, skills, and experience to effectively implement design of experiments in your research and work life. I have worked and consulted extensively in design of experiments and will incorporate those experiences in this course.
Course Description, Prerequisites & Course Modality¶
Course Description¶
Randomization and blocking, significance tests and confidence intervals, factorial design, applications of factorial designs, modeling building with least squares, and response surface methodologies
Course Prerequisite¶
MANE 2332 - Engineering Statistics, or MANE 3332 - Engineering Statistics, or equivalent
Course Modality¶
This course is offered as an online asynchronous course with voluntary weekly meetings.
Course Assignments & Learning Objectives¶
Learning Objectives¶
| Program Student Learning Outcome | Major Course Requirement/Major Assignment/Examination |
|---|---|
| A. Analyze simple comparitive experiments and experiments with a single factor | Homework |
| B. Select an appropriate experimental designs for experiments with one or more factor(s) | Homework, Factorial projects, RSM project |
| C. Select an appropriate model for design with one or more factor(s) | Homework, Factorial projects, RSM project |
| D. Evaluate statistical analyses of experimental designs | Homework, Factorial projects, RSM project |
| E. Assess the model adequacy of any experimental design | Homework, Factorial Projects, RSM project |
| F. Interpret model results | Homework, Factorial Projects, RSM project |
Course Assignments¶
There are three types of assignments that will be utilized in this course: homework, projects, and participation (3-2-1) assignments.
A Typical Week¶
| Day of Week | Activity |
|---|---|
| Monday Weekly Material posted in Brightspace | |
| Thursday | Optional Zoom Meeting at 7:00 pm |
| Friday | Previous Week's assignment(s) are due |
Tentative Assignment Dates¶
Students should expect to submit weekly homework assignments. In addition to the weekly homework assignments, there will be project assignments and 3-2-1 Assignments.
The tentative schedule for 3-2-1 Assignments is given in the table below.
| Week | Topic(s) |
|---|---|
| 5 | Chapters 1-3 |
| 9 | Chapters 4-7 |
| 10 | Chapter 8 |
| 12 | Chapters 10-11 |
| 16 | Remainder of course materials |
There will be two major projects: Fractional Factorial Design and Response Surface Methodology. The tentative schedule for the the projects is given in the table below.
| Project Assignment | Assignment Date |
|---|---|
| Fractional Factorial Design, part 1 | February 9, 2026 |
| Fractional Factorial Design, part 2 | March 30, 2026 |
| Response Surface Methodology | April 13, 2026 |
Assessment of Learning/Grading Policy¶
Your performance in this course will be assessed in the following manner:
| Component | % of Overall Grade |
|---|---|
| Homework | 25% |
| Fractional Factorial Projects | 30% |
| RSM Project | 30% |
| Participation | 15% |
Homework¶
There is only one method to learn and master engineering material - work problems. The best method to ensure that you excel in this course is to diligently complete all of your homework assignments. There will be representative homework problems assigned each week. Solutions will be posted in Brightspace one week after the due date of each homework assignment.
Fractional Factorial Project¶
You will complete a fractional factorial project in two components that are worth 30% of your overall course grade. The first project is to analyze the current operating condition of a manufacturing plant. The second project is to perform a fractional factorial design. The weighting of the two project grades will be announced during the semester.
Further, both components of the fractional factorial project must be submitted and students must receive a score of at least 60%. If a student fails to submit a component or any component has a score lower than 60%, they will receive a grade of F in the course
RSM Project¶
In lieu of a final examination, a response surface methodology (RSM) project will be completed by each student.
As with the Fractional Factorial Project, the RSM project must be submitted and students must receive a score of at least 60%. If a student fails to submit the RSP project or the RSM Project score is lower than 60%, they will receive a grade of F in the course
Participation¶
Embedded in the lesson materials will be various assignments such as 3-2-1 assignments that are more reflective in nature.
Letter Grade Assignment¶
An overall course average will be calculated using the weighting scheme specified above. Your course average will be a value between 0 and 100. Your final letter grade will be assigned using the following definition.
| Course Average | Letter Grade |
|---|---|
| 90 - 100 | A |
| 80 - 89 | B |
| 70 - 79 | C |
| <70 | F |
Late Work¶
Descriptions of each assignment, including due dates, will be provided throughout the course. All assignments should be submitted on their due date using the provided Brightspace drop box. The course policy for late work is a 10% penalty per day for work submitted after the deadline. After one week, no credit will be given for late work. No late work will be accepted after study days. There may be certain assignments where late work will not be accepted. This fact will clearly be stated in the apporpriate assignments.
Students who miss graded assignments will receive a grade of zero. If you are ill or have a serious problem that prevents you from submitting an assignment on the day it is due, please contact me prior to the due date (if possible) and we will arrange an alternative date.
Required Readings, Technology Needs, and Resource Materials (Required on all syllabi per HB 2504)¶
The following textbook is required for completion of Design of Experiments.
Required Textbook¶
Montgomery, Douglas C. (2019). Design and Analysis of Experiments, 10th edition. John Wiley & Sons. ISBN: 978-1-119-49244-3.
Technology¶
This course will extensively use R Markdown software for the design and analysis of experiments. R Markdown will be implemented in the RStudio IDE which is free software. One of the advantages of R Markdown on R Studio is the software works on Windows, Mac OS and Linux systems. The UTRGV vLabs also offers an older version of R Studio that can be run online utilizing a virtual machine. Additional information on R Markdown and R Studio will be provided during Week Two.
Tentative Calendar of Activities¶
| Week - Date | Topic |
|---|---|
| 1 - January 19 | Syllabus, Brightspace, Textbook and Chapter 1 |
| 2 - January 26 | R, R Studio, R Markdown |
| 3 - February 2 | Chapter 2 - Simple Comparitive Experiments |
| 4 - February 9 | Chapter 3 - One-way ANOVA |
| 5 - February 16 | Chapter 4 - Randomized Block Design |
| 6 - February 23 | Chapter 5 - Introduction to Factorial Designs |
| 7 - March 2 | Chapter 6 - 2^k Factorial Designs |
| 8 - March 9 | Chapter 7 - Blocking and Confounding |
| March 16-21 | Spring Break Holiday |
| 9 - March 23 | Chapter 8 - Fractional Factorial Designs |
| 10 - March 30 | Chapter 8 - Fractional Factorial Designs, continued |
| 11 - April 6 | Linear Regression |
| 12 - April 13 | Response Surface Methodology |
| 13 - April 20 | Response Surface Methodology, continued |
| 14 - April 27 | Miscellaneous topics |
| 15 - May 4 | no class meeting; study day (May 7) |
| 16 - May 14 | May 14 - RSM Project due |
Important University Dates¶
A subset of the Spring 2026 Academic calendar containing important dates is provided below. The entire academic calendar is available at https://www.utrgv.edu/_files/documents/admissions/utrgv-academic-calendar.pdf.
| Date | Event |
|---|---|
| January 19 | Martin Luther King Jr. Holiday. No classes |
| January 20 | First day of classes |
| January 25 | Last day to add a class or register |
| March 16 - 21 | Spring Break. No classes |
| April 3 - 4 | Easter Holiday. No classes |
| April 9 | Last day to drop a class or withdraw |
| May 7 | Study Day - No classes |
| May 8 - 14 | Final Exams |
| May 18 | Grades due at 3 pm |
Course Policies and Procedures¶
Recorded Material Policy¶
Should you elect to record your instruction, sample syllabus language is included here:
The use of classroom recordings is governed by the Federal Educational Rights and Privacy Act (FERPA), UTRGV's acceptable-use policy, and UTRGV HOP Policy STU 02-100 Student Conduct and Discipline. A recording of class sessions will be kept and stored by UTRGV, in accordance with FERPA and UTRGV policies. Your instructor will not share the recordings of your class activities outside of course participants, which include your fellow students, teaching assistants, or graduate assistants, and any guest faculty or community-based learning partners with whom we may engage during a class session. You may not share recordings outside of this course. As referenced in UTRGV HOP Policy STU 02-100 Student Conduct and Discipline, doing so may result in disciplinary action.
Use of Artificial Intelligence (AI) Technologies¶
Generative AI technologies are growing and evolving rapidly. We will have an opportunity to explore the benefits, challenges, and ethical decisions engineers encounter in the use of AI in this course. Generative AI will be incorporated into this course in a limited and specified manner.
Students are discouraged from using Generative AI technologies such as Chat GPT or Microsoft Copilot for homework. The goal of homework is to gain confidence and proficiency in your engineering analysis and Generative AI technologies will not be available for in-class tests. Submission of printed Generative AI output for student homework will result in a grade of zero and be reported as academic dishonesty.
For most laboratory assignments, Generative AI may not be submitted. Each laboratory assignment will contain a clear statement whether Generative AI can or cannot be used. Submission of code generated using Generative AI when not allowed will result in a grade of zero and will be reported as academic dishonesty.
ACADEMIC INTEGRITY¶
Academic integrity is fundamental in our actions, as any act of dishonesty conflicts as much with academic achievement as with the values of honesty and integrity. Violations of academic integrity include, but are not limited to: cheating, plagiarism (including self-plagiarism), and collusion; submission for credit of any work or materials that are attributable in whole or in part to another person; taking an examination for another person; any act designed to give unfair advantage to a student; or the attempt to commit such acts (Board of Regents Rules and Regulations, STU 02-100, and UTRGV Academic Integrity Guidelines). All violations of Academic Integrity will be reported to Student Rights and Responsibilities through Vaqueros Report It.
Student Support Resources¶
Technical Support¶
If you need assistance with course technology (Bright Space) at any time, please contact the Center for Online Learning and Teaching Technology (COLTT).
University Policy Statements¶
SEXUAL MISCONDUCT AND MANDATORY REPORTING (Required)¶
In accordance with UT System regulations, your instructor is a "Responsible Employee" for reporting purposes under Title IX regulations and so must report any instance of sexual misconduct, which includes sexual assault, stalking, dating violence, domestic violence, and sexual harassment to the Office of Title IX and Equal Opportunity (otixeo@utrgv.edu). More information can be found on the OTIXEO website. If students, faculty, or staff would like confidential assistance, or have questions, they can contact OAVP (Office for Advocacy & Violence Prevention).
STUDENT ACCESSIBILITY SERVICES¶
Student Accessibility Services has offices on Brownsville and Edinburg campuses. Visit the SAS web page to learn more and explore accessibility services.
STUDENTS WITH DISABILITIES¶
Students with a documented disability (physical, psychological, learning, or other disability which affects academic performance) who would like to receive reasonable academic accommodations should contact Student Accessibility Services (SAS)) for additional information. The student must apply for accommodations using the mySAS portal and is responsible for providing sufficient documentation of the disability to SAS. Upon submission of the request, students should expect to participate in an interactive discussion, or an intake appointment, with SAS staff. Accommodations may be requested at any time but are not retroactive, meaning they are valid moving forward after approval by SAS. Students should contact SAS early in the semester/module for guidance.
Students who experience a broken bone, severe injury, or undergo surgery may also be eligible for temporary accommodations. Please contact Student Accessibility Services (SAS)) for more information.
PREGNANCY, PREGNANCY-RELATED, AND PARENTING ACCOMODATIONS¶
Title IX of the Education Amendments of 1972 prohibits discrimination based on sex, which includes discrimination based on pregnancy, marital status, or parental status.
Students seeking accommodations related to pregnancy, pregnancy-related condition, or parenting should submit the request using the form found at Pregnancy and Parenting | UTRGV.
MANDATORY COURSE EVALUATION PERIOD¶
Students have the opportunity to complete an ONLINE evaluation of this course through Watermark Course Evaluations and Surveys, which may be accessed through my.UTRGV or the Bright Space course module. Course evaluations are used by the instructor to inform revisions of the course to ensure student success. Course evaluations are also used by the instructor for annual performance review, promotion applications, teaching award applications, among others.
Online evaluations will be available on or about:
Spring 2026 Regular Term: April 15 - April 21, 2026