Materials 3J03

Statistical Methods for Materials Engineers

Course Information 2015-2016

Instructor Room Email
Tim Dietrich N/A
TA Room Email
Daniel Osorio JHE A406


Link to Course Notes

Class Time and Location
Mondays 7:00 pm – 10:00 pm in JHE 326H

Calendar Description
This course will provide an introduction to probability and univariate data analysis. Topics will also include linear regression analysis, design of experiments (including factorial and optimal design), and statistical process control. Emphasis will be on the analysis of industrial problems.

Course Topics
1. Visualizing data: creating high-density, efficient graphics that highlight the data honestly.
2. Univariate data analysis: probability distributions and confidence intervals.
3. Least squares regression modeling: correlation, covariance, ordinary and multiple least squares models. Enrichment topics may be covered, time permitting.
4. Design and analysis of experimental data and response surface methods for continual process improvement and optimization.
5. Process monitoring, or statistical process control (SPC), for monitoring process behaviour.

Course Objectives
At the conclusion of this course, the student will be able to:
• Understand that all data has variability and that we want to separate that variability into information (knowledge) and error (unknown structure, noise, randomness).
• Interpret univariate data statistics (mean, median, standard deviation), testing for significant differences, and calculating and interpreting confidence intervals.
• Understand and use process monitoring charts (including Shewhart charts or control charts).
• Least-squares regression models: how to fit and especially how to interpret them; understand the confidence limits and model limitations.
• Be able to design an experiment and then interpret experimental data.

Primary Text Book
There is no official course textbook. We will be using the first 5 chapters from the book Process Improvement Using Data that can be downloaded for free at
This work is the copyright of Kevin Dunn and portions of MATLS 3J03 are also the copyright of Kevin Dunn. The book was written specifically for a course similar to this one. It is your responsibility to print out these notes and bring them to class and exams. The copyright to the book and materials is held by Kevin Dunn, but it is licensed to you under the permissive Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)3 license.

Other Useful Resources
1. G.E.P. Box, J.S. Hunter, and W.G. Hunter, Statistics for Experimenters - Design, Innovation and Discovery, 2nd edition, Wiley. ISBN: 978-0471718130.
2. D.C. Montgomery and G.C. Runger, Applied Statistics and Probability for Engineers.

Assignments: 25% (5 assignments—these can be completed by yourself or in groups of two students)

Experimental report: 10% (an experiment that you do yourself or in groups of two students; due last class in Nov.)

Midterm Test: 20% (October 26 in class)

Final Exam: 45% (will be scheduled between December 9 and December 22)

Late Assignments
Late assignments will be penalized by deducting 25% per day for every late day. Emergencies do arise, so each person has 2 "late day" credits for assignments. This means that you can hand in one assignment 2 days late, or two assignments each 1 day late, without penalty.

Important Notes

Class participation: Please bring a calculator to every class.
Course software: Use of a computer is required in the course. The software MINITAB will be used ( The software is available in the JHE 233A/234 and BSB 241/242 computer labs.

You may use other statistical analysis software you are comfortable using; you should not use Microsoft Excel.

Out-of-class access
The instructor will attempt to answer most questions by text/email. Keep in mind that email might only be checked in the late evenings. If you wish to meet in person, we can arrange a time and location to meet.

Academic Integrity
You are expected to exhibit honesty and use ethical behaviour in all aspects of the learning process. Academic credentials you earn are rooted in principles of honesty and academic integrity.
Academic dishonesty is to knowingly act or fail to act in a way that results in or could result in unearned academic credit or advantage. This behaviour can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: “Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from the university.
It is your responsibility to understand what constitutes academic dishonesty. For information on the various types of academic dishonesty please refer to the Academic Integrity Policy, located at
The following illustrates only three forms of academic dishonesty:
1. Plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained.
2. Improper collaboration in group work: this point is particularly important and will be strongly penalized in this course.
3. Copying or using unauthorized aids in tests and examinations.

Antirequisite(s): STATS 3Y03