MANE 6313.90L: Design of Experiments
Syllabus
Spring 2025
Subject to any new Texas legislative mandate changes.
Course Information (Required on all syllabi per HB 2504)
Meeting Times: R 7:55 - 10:25 PM Meeting Location: Online
Course Modality: Online Asynchronous Course (OASYNC)
Instructor Information (Required on all syllabi per HB 2504)
Instructor Name: Dr. Douglas Timmer
UTRGV E-mail: douglas.timmer@utrgv.edu Office Phone: (956) 665-2608 Office Location: EENGR 3.258 Office Hours: M-R 9:30 - 10:45 am or by appointment
Course Description, Prerequisites & Course Modality (Required)
Special Designations
- None
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
Prerequisites
MANE 2332 - Engineering Statistics, or MANE 3332 - Engineering Statistics, or equivalent
Course Modality
This course is offered as an online asynchronous course (OASYNC).
Course Assignments & Learning Objectives (Required on all syllabi per HB 2504)
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 Blackboard | |
| Thursday | Optional Zoom/Face-to-Face Meeting at 7:55 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 13, 2025 |
| Fractional Factorial Design, part 2 | March 27, 2025 |
| Response Surface Methodology | April 10, 2025 |
Assessment of Learning/Grading Policy (Required on all syllabi per HB 2504)
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 Blackboard one week after the due date of each homework assignment.
Fractional Factorial Projects
You will have two individual project assignments 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.
RSM Project
In lieu of a final examination, a response surface methodology project will be completed by each student.
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 Blackboard 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 mayl 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. E-book cost: $120.00, E-book rental (120 days): $42.00.
Technology
This course will extensively use R Markdown software for the design and analysis of experiments. This is the first semester that R Markdown will be used instead of Minitab. 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 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 (Required on all syllabi per HB 2504)
Calendar of Lecture Activities
| Week | Topic |
|---|---|
| 1 - January 23 | Syllabus, Blackboard, Textbook and Chapter 1 |
| 2 - January 30 | R, R Studio, R Markdown |
| 3 - February 6 | Chapter 2 - Simple Comparitive Experiments |
| 4 - February 13 | Chapter 3 - One-way ANOVA |
| 5 - February 20 | Chapter 4 - Randomized Block Design |
| 6 - February 27 | Chapter 5 - Introduction to Factorial Designs |
| 7 - March 6 | Chapter 6 - 2^k Factorial Designs |
| 8 - March 13 | Chapter 7 - Blocking and Confounding |
| March 17-23 | Spring Break Holiday |
| 9 - March 27 | Chapter 8 - Fractional Factorial Designs |
| 10 - April 3 | Chapter 10 - Fitting Regression Models |
| 11 - April 10 | 11.5 - 11.7, 14.4 - 14.5 |
| 12 - April 17 | Chapter 9 - 3-level Designs |
| 13 - April 24 | DOE for machine learning and data science |
| 14 - May 1 | DOE in Biomedical applications |
| 15 - May 8 | no class meeting; study day |
| 16 - May 15 | May 15 - RSM Project due |
Important University Dates
A subset of the Spring 2025 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 20 | Martin Luther King Jr. Holiday. No classes |
| January 21 | First day of classes |
| January 26 | Last day to add a class or register |
| March 17 - 23 | Spring Break. No classes |
| March 18-19 | Easter Holiday. No classes |
| April 10 | Last day to drop a class or withdraw |
| May 8 | Study Day - No classes |
| May 9-15 | Final Exams |
| May 19 | Grades due at 3 pm |
Course Policies and Procedures
LEARNING AND TEACHING ENVIRONMENT
Welcome to Engineering Statistics (MANE 3332)! I’m Dr. Doug Timmer, and I’m excited to guide you through this essential course. We’ll explore key statistical methods like probability distributions, hypothesis testing, and regression analysis, all crucial for solving engineering problems. With over 25 years of experience in academia and industry, I’ll help you gain practical skills using tools like R and Markdown. My goal is to make learning engaging and relevant, preparing you for success in your engineering career. Let’s make this a great semester!
ATTENDANCE
Attendance is expected and students are expected to be actively engaged in the course. Weekly optional Zoom meetings will be conducted. Please review the UTRGV attendance policy.
When setting your attendance policy, please consider extenuating circumstances. Accommodations related to long-term complications from medical conditions should go through SAS. Students should contact the instructor in advance of the excused absence and arrange to make up missed work or examinations.
Recorded Lectures
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.
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 Practice Problems. The goal of Practice Problems is to gain confidence and proficiency in your engineering analysis and Generative AI technologies will not be available for in-class tests. Use of Generative AI for Online Quizzes is prohibited and will result in a grade of zero and be reported as academic dishonesty. AI tools for Research will be incorporated in the preparation of the Technical Report.
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 (optional)
If you need assistance with course technology (Blackboard) at any time, please contact the Center for Online Learning and Teaching Technology (COLTT).
University Policy Statements
We are committed to your personal, academic, and professional success; please know you can reach out to me for questions and/or I can help you identify the resources you need. UTRGV offers student support resources designed to contribute to your well-being and academic excellence.
Students seeking academic help in their studies can use university resources in addition to an instructor's office hours. University Resources include the Advising Center, Career Center, Counseling Center, Learning Center, and Writing Center. These centers provide services such as tutoring, writing help, counseling services, critical thinking, study skills, degree planning, and connections student employment (through Handshake and HR Student Employment). In addition, services, such as the Food Pantry are also provided. Locations are listed below.
| Center Name | Brownsville Campus | Edinburg Campus |
|---|---|---|
| Advising Center AcademicAdvising@utrgv.edu | BMAIN 1.400 (956) 665-7120 | EITTB 1.000 (956) 665-7120 |
| Career Center CareerCenter@utrgv.edu | BINAB 1.105 (956) 882-5627 | ESTAC 2.101 (956) 665-2243 |
| Counseling Center Counseling@utrgv.edu Mental Health Counseling and Related Services List | BSTUN 2.10 (956) 882-3897 | EUCTR 109 (956) 665-2574 |
| Food Pantry FoodPantry@utrgv.edu | BCAVL 101 & 102 (956) 882-7126 | EUCTR 114 (956) 665-3663 |
| University Library www.utrgv.edu/library circulation@utrgv.edu | BLIBR (956) 882-8211 | ELIBR (956) 665-2585 |
| Learning CenterLearningCenter@utrgv.edu | BMSLC 2.118 (956) 882-8208 | ELCTR 100 (956) 665-2585 |
| Writing Center WC@utrgv.edu | BUBLB 3.206 (956) 882-7065 | ESTAC 3.119 (956) 665-2538 |
STUDENT ACCESSIBILITY SERVICES
Student Accessibility Services staff can be contacted at either campus to learn about and explore accessibility services.
| Campus: | Brownsville | Edinburg |
|---|---|---|
| Location: | Music and Learning Center (BMSLC, 1.107 | University Center (EUCTR, 108) |
| Phone: | phone (956) 882-7374 | phone (956) 665-7005 |
| ability@utrgv.edu | ||
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 sex discrimination, 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.
SEXUAL MISCONDUCT AND MANDATORY REPORTING
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).
MANDATORY COURSE EVALUATION PERIOD
Students have the opportunity to complete an ONLINE evaluation of this course, accessed through your my.UTRGV account. 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 and promotion applications, teaching award applications, among others.
Online evaluations will be available on or about:
| Term | Dates |
|---|---|
| Spring Module 1 (7 weeks) | February 19 - 25, 2025 |
| Spring Regular Term 2025 | April 16 - May 7, 2025 |
| Spring Module 2 (7 weeks) | April 16 - 22, 2025 |