Syllabus
MANE 3332.05: Engineering Statistics
Syllabus
Fall 2025
Subject to any new Texas legislative mandate changes.
Course Information
Meeting Days, Time, Location: TR 3:30 - 4:45 PM, EENGR 1.242
Campus Maps](https://www.utrgv.edu/maps/)
Course Modality: Traditional Face-to-Face Courses (TR)
Instructor Information
Instructor Name: Dr. Doug Timmer
UTRGV E-mail: douglas.timmer@utrgv.edu Office Phone: 956-665-2608 Office Location: ENGR 3.258 Office Hours: M - R 2:00 PM - 3:15 PM
Welcome and Teaching Philosophy
Statistics is one of my favorite courses to teach! I have a master's degree in Statistics and industrial experience working as a statistician. Further, I was a faculty member in a Statistics' department for one year in New Zealand. My Ph.D. in Industrial Engineering focused on Statistical Quality Control. I worked five years for a start-up company that employed neural-networks (advanced statistical tools) to model, predict, and control manufacturing processes. While working in this startup, I was co-inventor on 13 US patents which were all statistics-related.
Course Description, Prerequisites & Course Modality
Course Description
Fundamentals of probability, commonly encountered density functions, distribution functions, statistical tests and experimental designs as used in manufacturing and product design. Includes use of microcomputer-based statistical analysis software.
Prerequisites
MATH 2413 and credit for or enrollment in MATH 2414
Course Modality
This course is offered as a traditional face-to-face course.
Course Assignments & Learning Objectives
| Student Learning Outcome | Program Student Learning Outcomes | Major Course Requirement/Major Assignment/Examination |
|---|---|---|
| SLO-1. Apply binomial, hypergeometric, Poisson and normal distributions to solve manufacturing engineering problems | ABET SO 1, an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | Practice Problems, Online Quiz, Mid-term Exam |
| SLO-2. Identify correct distribution for solving common manufacturing engineering problems | ABET SO 6, an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | Mid-term Exam |
| SLO-3. Apply appropriate graphical and numerical tools to analyze manufacturing engineering problems | ABET SO 1, an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | Final Exam |
| SLO-4. Perform statistical test of hypothesis for single and two samples and construct confidence intervals for single samples and two samples | ABET SO 6, an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | Practice Problems, Online Quizzes, Final Exam |
| SLO-5. Identify correct test of hypothesis and confidence interval formula for manufacturing engineering problems | ABET SO 6, an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | Final Exam |
| SLO-6. Perform simple and multiple linear regression analysis | ABET SO 6, an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | Final Exam |
Course Assignments
I expect all students to fully participate in the course and exhibit professional behavior. There is only one method to learning and master material of a mathematical or statistical nature - work problems. Your learning will be evaluated using the following types of assignments.
Attendance
You are expected to attend all course lectures. Attendance will recorded using bubble sheets provided by Dr. Timmer. Attendance will be taken after the twelfth class day.
Practice Problems
Most major topics will have one or more sets of practice problems. Practice problems are online evaluations that evaluate your ability to perform a statistical analysis. Practice Problems are not timed and have an unlimited number of attempts. Your highest score for each Practice Problem will be used in calculating your Practice Problems grade. Practice Problems will remain available until the course final exam, and are an excellent review resource..
Online Quizzes
Online quizzes are essentially practice problems reused for quizzes. After a set of practice problems are assigned, an online quiz generated by the same program used for the practice problems will be assigned the next class period. The Online Quizzes will be 30 minutes in duration, a single attempt will be allowed, and a deadline will be specified.
Examinations
There will be two major examinations in this course: a mid-term exam and a final exam. Students can prepare a single, handwritten 4 inch by 6 inch notecard for each exam. The exams will be closed book. Any needed reference tables will be provide by Dr. Timmer.
Assessment of Learning/Grading Policy
Course Average
Your course average will be calculated using the proportions specified in the table below.
| Component | % of Course Average |
|---|---|
| Attendance | 10% |
| Practice Problems | 20% |
| Online Quizzes | 20% |
| Mid-term Exam | 25% |
| Final Exam | 25% |
Letter Grade Assignment
UTRGV's grading policy is to use straight letter grades (A, B, C, D, or F) (no + or -). Your final letter grade will be assigned using the definition provided in the table below.
| Course Average | Letter Grade |
|---|---|
| 90 - 100 | A |
| 80 - 90 | B |
| 70 - 80 | C |
| 60 - 70 | D |
| <60 | F |
Late Work
Descriptions of each assignment, including due dates, will be provided throughout the course. All assignment should be submitted on or before their due date through Brightspace. The following rules apply:
- 10% penalty per day for work submitted after the due date,
- after one week, no credit will be given for late work,
- no late work will be accepted after study day,
- certain assignments will will not be accepted as late work (these assignments will clearly be labelled).
Extra Credit Policy
It is my personal policy to not provide extra credit in this course.
Attendance Policy
All students are expected to attend all scheduled course meetings. Attendance will be taken after the twelfth class day.
Missing Assignments
Student 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 arrangements for an alternative due date will be made.
Required Readings, Technology Needs, and Resource Materials
Textbook - required
Students are encouraged to explore renting a digital copy to reduce textbook costs.
Applied Statistics and Probability for Engineers, 7th edition
Author(s): Douglas. C. Montgomery and Geroge C. Runger
Edition: 7th
Copyright Year: 2018
Publisher: John Wiley and Sons
International Standard Book Number (ISBN-13): 978-11194095333
International Standard Book Number (ISBN-10): 119409535
Additional Content Needed: None
Open Educational Resource (OER): No
Please note that WileyPlus is not required for this course.
Brightspace
This course will utilize Brightspace for its course management software. Brightspace is maintained by the Center for Online Teaching and Technology (COLTT). You can access Brightspace through any Java-enabled web browser. Suitable web browsers are installed in the Intel lab in the Engineering build and other computer laboratories throughout campus.
To access Brightspace, you will need an UTRGV e-mail account. Most students should have an UTRGV email account. Brightspace was adopted as the official learning management system for UTRGV in Summer 2025. For some students, this will be your time using Brightspace.
R
This course will use R or more precisely R Markdown to perform some statistical analyses. R Markdown is available in the open source application RStudio IDE provided by Posit. Detailed instructions for the installation and use of R will be provided later in the course. RStudio IDE is an open-source application (free) that will run on Windows, Mac and Linux operating systems. RStudio is also available via UTRGV Labs.
Tentative Calendar of Activities
A tentative weekly schedule of the lecture activities (MANE 3332.01) is provided in the table below.
| Week, Date, Theme | Learning Objective(s) | Readings Due | Assignments Due |
|---|---|---|---|
| 1, 9/1 - 9/7, Course Overview, Syllabus, Intro. to Statistics | Chapter 1 | Purchase textbook | |
| 2, 9/8 - 9/14, Probability | Chapter 2 | PP, Q | |
| 3, 9/15 - 9/21, Probability and Discrete Distributions | SLO-1, and SLO-2 | Chapters 2 and 3 | PP, Q |
| 4, 9/22 - 9/28, Discrete Distributions | SLO-1, and SLO-2 | Chapter 3 | PP, Q |
| 5, 9/29 - 10/5, Discrete Distributions and Continuous Distributions | SLO-1, and SLO-2 | Chapters 3 and 4 | PP, Q |
| 6, 10/6 - 10/12, Continuous Distributions | SLO-1, and SLO-2 | Chapter 4 | PP, Q |
| 7, 10/13 - 10/19, Introduction to R, Midterm Review | SLO-3 | Outside Materials | |
| 8, 10/20 - 10/26, Midterm Review and Chapter 6 | SLO-3 | Chapter 6 | Midterm Exam |
| 9, 10/27 - 11/2, Assorted Topics | Chapter 5 | PP, Q | |
| 10, 11/3 - 11/9, Definitions and Interval Estimates | SLO-4, and SLO-5 | Chapters 7 and 8 | PP, Q |
| 11, 11/10 - 11/16, Interval Estimates | SLO-4, and SLO-5 | Chapter 8 | PP, Q |
| 12, 11/17 - 23, Interval Estimates and Hypothesis Tests | SLO-4, and SLO-5 | Chapters 8 and 9 | PP, Q |
| 13, 11/24 - 11/30, Hypothesis Tests (and Thanksgiving) | SLO-4, and SLO-5 | Chapter 9 | PP, Q |
| 14, 12/1 - 12/7, Hypothesis Tests and Linear Regression | SLO-4, SLO-5, and SLO-5 | Chapters 9, 10, and 11 | PP, Q |
| 15, 12/8 - 12/14, Review and Study Day | SLO-6 | ||
| 16, 12/15 - 12/19, Final Exam Week |
The Final Exam is scheduled for Thursday December 18, 1:15 - 3:00 PM.
Please note that all reading assignments and assessment activities dates are subject to change. These dates are my best estimate of how the course will proceed but, usually, changes are inevitable.
Recorded Material Policy
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.
The use of AI technologies is prohibited for the preparation of any materials to be submitted for a grade. Properly used, AI technologies can be a powerful and effective learning tool. Used improperly, AI technologies can provide false answers and rob you of the time and effort that is required to learn a skill or topic.
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:
Fall 2025 Regular Term November 19 -- December 18, 2025
Important Course Dates
A subset of the events in the academic calendar is provided below.
| Date | Event |
|---|---|
| September 1, 2025 | Labor Day Holiday |
| September 2, 2025 | Fall classes begin |
| September 17, 2025 | Census Day |
| November 13, 2025 | Last day to drop or withdraw |
| November 27 - 29, 2025 | Thanksgiving Holiday. No Classes |
| December 11, 2025 | Study Day. No classes |
| December 12 - 18, 2025 | Final Exams |
| December 18, 2025 1:15 - 3:00 PM | MANE 3332.05 Final Exam |
| December 19 - 20, 2025 | Commencement Exercises |
| December 22, 2025 | Grades are due at 3 pm |
I volunteer as an ABET program evaluator and will be on an ABET visit from December 13 - 17. During this time, I will not be available to answer any questions and will have very limited time for non-ABET communications.