Spring 2021 - MSE 210 D100

Engineering Measurement and Data Analysis (3)

Class Number: 3998

Delivery Method: Remote


  • Course Times + Location:

    Mo 8:30 AM – 10:20 AM

    We 8:30 AM – 9:20 AM

  • Exam Times + Location:

    Apr 22, 2021
    12:00 PM – 3:00 PM

  • 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, ENSC 280 or PHYS 231 may not take MSE 210 for further credit.


Course Website: http://canvas.sfu.ca (log in using your SFU computing ID)

Course Schedule
Lecture/Tutorial: Mondays 8:30-10:20am and Wednesdays 8:30–9:20am, REMOTE
Lab: Mondays and Wednesdays 4:30–7:20pm, SRYC 4270/4290

Midterm Exam: Date/time TBD, REMOTE
Final Exam: Date/time TBD, REMOTE

Remote Office Hours:
Mondays 10:30-11:30am and Wednesdays 9:30-10:30am, after class

Course Outline:

(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 (REMOTE; Online; Assignment style reporting)
Lab 2: Engineering measurement (REMOTE; Online; Assignment style reporting)

Lab 3: Hypothesis testing & Empirical modeling (FACE-TO-FACE @ SRYC 4270/4290; Hands-on, Full report)

Lab 4: Design of experiments (REMOTE; Hands-on; Full report)


Course Objective:

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 10%
  • Lab Reports 10%
  • Midterm 30%
  • Final 50%



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.

Academic Integrity

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:



  1. Textbook:

    Engineering Statistics, 5th Edition Montgomery, Runger, and Hubele, Wiley, 2011

    Supplementary Books:
    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

Registrar Notes:


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 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges 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. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).