# Spring 2022 - MSE 210 D100

## Overview

• #### 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

• #### Instructor:

Krishna Vijayaraghavan
krishna@sfu.ca
1 778 782-9077
• #### Prerequisites:

PHYS 141 or equivalent. MATH 150 or MATH 151.

## Description

#### CALENDAR DESCRIPTION:

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 DETAILS:

Course Schedule
:
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

Office Hours:
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

Laboratory:

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:

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 15%
• Lab Reports 10%
• Midterm 30%
• Final 45%

#### NOTES:

Assessment:

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:

## Materials

1. Textbook:

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