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Course outline (2012)

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Class date(s): 05 January 2012
Nuvola mimetypes pdf.png (PDF) Course outline
Nuvola mimetypes pdf.png (PDF) Projector overheads
Video material
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Video timing

  • 00:00 to 43:45 Administrative overview (software, grading, etc)
  • 43:45 to 72:15 Overview of course content

[edit] Materials

Class date: 5 January 2012
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Kevin Dunn, (no office on campus)

Teaching assistants

Yasser Ghobara, Room JHE-370, ext 22008,, office hours: Tuesday, 4-5 pm, Thursday, 11am-12

Pedro Castillo, Room JHE-370,, office hours: Wednesday, 5-6pm, Thursday, 4-5pm

Class time and location

T13, room 127. Thursday evenings from 18:30 to 21:30

About the course

Official description

Linear regression analysis in matrix form, non-linear regression, multi-response estimation, design of experiments including factorial and optimal designs. Special emphasis on methods appropriate to engineering problems.

What you must be able to demonstrate by the end of the course

  • Understand that all data has variability: we want to separate that variability into information (knowledge) and error (unknown structure, noise, randomness).
  • Interpret confidence intervals and univariate data statistics (mean, median, histograms, significant differences)
  • Understand and use process monitoring charts
  • Least-squares models: how to fit and especially how to interpret them, understand the confidence limits and model limitations.
  • Be able to design your own experimental program and then also interpret experimental data.
  • Understand the principles of latent variable methods for engineering data.


A basic course in statistics that covers probability, means, variances, confidence intervals and linear regression. However, all these topics are covered again in this course, focusing on their practical application to engineering problems.

Course materials

The course website will be permanently available:

Course materials, assignments and solutions will be available from the website. Course announcements will only be posted to the main page of the website - students are expected to check the website at least 3 times per week.

Required textbook

There is no official course textbook. We will be using the instructor's own material from his book, Process Improvement using Data. The book was written specifically for this course, and will be available as a PDF from the course website. It is your responsibility to print out these notes and bring them to class.

If you prefer not to print it yourself, the Titles Bookstore will have a limited number of printed copies, available for only the cost of printing the PDF.

Recommended readings

If you would like to buy one book to supplement the course material, I highly recommend the first one, Box Hunter and Hunter, for its practical engineering perspectives on data analysis.

  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.

Other reference texts are listed on the course website and are generally available in Thode Library.

Course outline (differs somewhat from the official description)

The course is divided into 6 main sections, taught over 12 weeks.

  1. Visualizing data: creating high-density, efficient graphics that highlight the data honestly.
  2. Univariate data analysis: Probability distributions and confidence intervals
  3. Process monitoring, aka statistical process control (SPC), for monitoring process behaviour.
  4. Least squares regression modelling: correlation, covariance, ordinary and multiple least squares models. Enrichment topics will be covered, time permitting.
  5. Design and analysis of experimental data and response surface methods for continual process improvement and optimization.
  6. Introduction to latent variable modelling: a general overview of latent variable models and their use in (chemical) engineering processes.

Several enrichment topics are covered throughout the course: robust methods, cross-validation for model assessment, nonparametric methods, real-time application of the above methods, correlation and causality, and missing data handling.


To assess your understanding of the course materials, the grading for the course will be:

Component Fraction Notes
Assignments 20% Expect around 7 assignments; can be completed individually, or in groups of 2 or less (4C3 and 6C3), or by yourself.
Midterm exam 1 15% A 2.5 hour written exam, on 16 February, before the midterm break.
Midterm exam 2 20% A take-home exam, using software, over a 5-day period. Also includes an experiment that you have to do with your group, ahead of time, and analyze. Due on 22 March.
Final exam 45% A written exam, lasting 3 hours.

6C3 students will have extra questions on all assignments and exams.

Policies regarding grading

Important notes

Class participation: Please bring a calculator to every class.

Course software

Use of a computer is required in the course. The R-language ( will be used, and is a freely available software package that runs on Linux, Apple and Windows computers. The software is available in the 4th year Chemical Engineering computer labs. Minitab (you can rent a 6-month version very cheaply), MATLAB, or Python may be used as well; you should not use Microsoft Excel. Where time permits, the TA and the instructor will post solutions in these languages. More details are posted on the course website.

Out-of-class access

Since the course instructor does not have an office on campus, office hours will be before and after the class time, or arranged by appointment.

The TAs for this course can be contacted by email - please see their addresses above. Try to send email from your McMaster account - email from personal accounts are sometimes discarded by spam filters.

Disclaimer: The above outline may be modified, as circumstances change.

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 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.

Important dates

A list of tentative dates:

Date Description
5 January 2012 Overview class: review of course content and administrative issues
5 January 1. Data visualization section starts
12 January Assignment 1 due
12 January 2. Univariate data analysis section starts
19 January Assignment 2 due
26 January 3. Process monitoring section starts
26 January Assignment 3 due
2 February 4. Least squares modelling section starts
2 February Assignment 4 due
9 February Assignment 5 due
16 February Written midterm
20-24 February Midterm break
1 March Assignment 6 due
1 March 5. Design and analysis of experimental data section starts
8 March Assignment 7 due
22 March Take-home exam and experimental project due
29 March 6. Latent variable methods section starts
5 April Optional review class
7 April Exams start

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