Spring 2022 - MSE 210 D100
Engineering Measurement and Data Analysis (3)
Class Number: 1007
Delivery Method: In Person
Course Times + Location:
Tu 8:30 AM – 10:20 AM
SRYC 3310, Surrey
Fr 8:30 AM – 9:20 AM
SRYC 3310, Surrey
Exam Times + Location:
Apr 19, 2022
3:30 PM – 6:30 PM
SRYC 2600, Surrey
1 778 782-9077
Prerequisites:PHYS 141 or equivalent. MATH 150 or MATH 151.
An introduction to methods to collect and analyse engineering data. Topics include the Engineering data representation, Discrete and continuous probability density functions, Engineering measurements, Error analysis, Introduction to sensor interfaces, Introduction to physical sensors, Introduction to sensor signal conditioning, Noise, Test of hypotheses, Linear and nonlinear regression, and Design of experiments. Students with credit for SEE 241 or ENSC 280 may not take MSE 210 for further credit.
Course Website: http://canvas.sfu.ca (log in using your SFU computing ID)
Lecture/Tutorial: Tuesday 8:30-10:20am and Friday 8:30–9:20am, SRYC 3310
Lab: Mondays or Tuesday 4:30–7:20pm, SRYC 4270
Midterm(s) Exam: Date/time TBD
Final Exam: Date/time TBD
(1) Introduction and Data representation
Introduction to engineering measurements, Dot plots, Stem-and-Leaf diagrams, Histograms, Box plots, Time series plots, Scatter plots
(2) Introduction to probability
Population and sample, Random variables, Mean and variance, Functions of random variables
(3) Probability distribution functions
Discrete distributions: Binomial, Poisson
Continuous distributions: Normal, Lognormal, Exponential, Weibull, Gamma Normal approximations to Binomial and Poisson distributions
(4) Error analysis
Reporting and using uncertainties, Error propagation, Random and systematic errors
(5) Engineering measurement
Sensitivity, Accuracy, Precision, Resolution, Quantization, Noise
(6) Hypothesis testing
Point estimation, z-test, t-test, χ2 test, F-test, Testing for the goodness of a fit
(7) Empirical models
Simple linear regression, Multiple regression, Least-square fitting to polynomial models
(8) Design of experiments Factorial analysis
(9) Statistical process control
X_bar and R charts, Process stability and control
There are four laboratory exercises for this course. A hybrid lab model will be used this year, for which three labs are conducted remotely and one lab is conducted face-to-face on campus. Lab manuals will be posted on Canvas before each session. Laboratory report requirements, background, and a lab schedule will be made available in the third week of term. During the lab period, students will work individually or in groups as assigned. Lab reports are due one week after each lab session.
Lab 1: Coin toss (Assignment style reporting)
Lab 2: Engineering measurement (Assignment style reporting)
Lab 3: Hypothesis testing & Empirical modeling (SRYC 4270; Hands-on, Full report)
Lab 4: Design of experiments (Hands-on; Full report)
COURSE-LEVEL EDUCATIONAL GOALS:
This course provides an introduction to methods used in the engineering profession to collect and analyze data. At the conclusion of this course:
Students should be able assess the presence of variability in real engineering problems and comprehend the importance of the statistical approach while making a decision.
Students should be able to employ fundamental statistical tools that are required in statistical practice and empirical research.
Students should gain the experience of analyzing experimental data collected in the laboratory sessions.
- Assignments 15%
- Lab Reports 10%
- Midterm 30%
- Final 45%
The midterm and final exams will be conducted remotely and invigilated by video. Both will be open book examinations of the course material. Additionally, a standardized formula sheet will be provided for reference. The final numerical score will be transferred to a letter grade following the Letter Grading Scheme described in the University Calendar.
Simon Fraser University is committed to creating a scholarly community characterized by honesty, civility, diversity, free inquiry, mutual respect, individual safety and freedom from harassment and discrimination. Any form of academic dishonesty or cheating will not be tolerated. For further information, please review SFU’s policies on academic integrity: http://www.sfu.ca/policies/Students/
Copying of others’ work is referred to as plagiarism and will not be tolerated. For more information, please visit:
Introductory Statistics (2018 version), B. Illowsky and S. Dean et al. [Freely available online https://assets.openstax.org/oscms-prodcms/media/documents/IntroductoryStatistics-OP_i6tAI7e.pdf]
A First Course in Design and Analysis of Experiments (2010 version), G. W. Oehlert, [Freely available online http://users.stat.umn.edu/~gary/book/fcdae.pdf]
Supplementary Books (Optional):
Engineering Statistics, 5th Edition Montgomery, Runger, and Hubele, Wiley, 2011
An Introduction to Error Analysis, 2nd Edition
Taylor, University Science Books, 1997
Applied Statistics and Probability for Engineers, 7th Edition Montgomery and Runger, Wiley, 2018
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
TEACHING AT SFU IN SPRING 2022
Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place. Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (firstname.lastname@example.org or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.