Course Syllabus
ST362 – Regression Analysis
Mathematics, Science, Waterloo
Spring | 2025
I acknowledge that in Kitchener, Waterloo, Cambridge and Brantford we are on the traditional territory
of the Neutral, Anishnaabe, and Haudenosaunee peoples.
Instructor Information
Dr. Shazia Azeem|sazeem@wlu.ca
Weekly Office Hours: Mondays 11:30am-12:30pm By Appointment
Work number: +1 226-243-8443
Course Location: SB107
Lecture Time: Mondays & Wednesdays 10.00 am to 11.20 am
Delivery Mode: In-Person
Lab Coordinator Information
Dr. Xuyang Ma | LH3060 | xma@wlu.ca
Lab Time: Wednesdays 08:30 AM - 09:50 AM (bi-weekly)
Teaching Assistant Information
Timothy Marsh | mars1283@mylaurier.ca
Weekly Office Hours: To be determined
Course Information
Calendar Description
Regression analysis including estimation, hypothesis testing, analysis of variance, variable selection
techniques; regression diagnostics; generalized linear regression; nonlinear regression; nonparametric
regression.
Pre-requisites : MA122 or MA123; and ST260 or (ST259 and one of ST230, ST231).
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Course Goals and Learning Outcomes
Regression analysis is the most important and useful statistical analysis tool in data science. In this
course, students will learn how to relate an outcome (or outcomes) to a set of predictors of interest. In
detail, students will learn how to conduct parameter estimation, statistical inference, analysis of
variance, residual analysis, model diagnostics and variable selection for linear regression models.
Further, students will be exposed to a wider range of concepts of regression models such as nonlinear
regression models, generalized linear models as well as nonparametric regression models. Students also
will learn the popular statistical language, R, and will be able to apply the knowledge they study in the
course to analyze real-world data in their projects.
Course Tools and Learning Materials
-
Calculators: You will require a cordless, non-programmable scientific calculator. Calculators may
be used during the final.
-
Computer Language: A statistical computer package called R (or its various versions) is used as
our programming language for in-class demonstrations and labs. The software can be accessed
Jupyter Notebook directly downloaded from the following websites R, RStudio, R Commander.
-
Textbook: Applied Regression Analysis (third edition) by Norman R. Draper and Harry Smith. A
digital version is available in the content on MyLS course website.
-
Reference Textbooks:
Item
o
Montgomery, D.C. and Peck, E.A., Introduction to Linear Regression Analysis, (Fifth
Edition), 2012. Wiley & Sons, Inc.
o
A. J. Dobson and A. Barnett. An Introduction to Generalized Linear Models. (Third
Edition), 2008. CRC press.
o
B. Abraham and J. Ledolter. Introduction to Regression Modeling. 2006. Brooks\cole,
Cengage Learning.
o
G. James, D. Witten, T. J. Hastie and R. J. Tibshirani. An Introduction to Statistical
Learning with Application in R. 2013. Springer.
o
Venables et al. An Introduction to R. 2017.
Required or
Optional
Cost
Current or
Most
Recent
Cost*
Notes/Features (i.e. physical copy, virtual copy,
second-hand availability, special features, etc.)
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Textbook:
Applied
Regression
Analysis
(third
edition) by
Norman R.
Draper and
Harry Smith
Required
N/A
N/A
Available Through library as an electronic copy
scientific
calculator
Required
n/a
n/a
Any non-programable scientific calculator
-
Course Website: Material related to this course, including lecture slides, assignments, exam,
projects information, etc., will be posted on the MyLearningSpace site:
https://mylearningspace.wlu.ca. Students are expected to check MyLearningSpace on a
frequent and regular basis.
Student Evaluation
Assessment
Weighting
Assignments (5)
15%
Labs
15%
Midterm (Wednesday, June 18, 2025)
20%
Project
10%
Final Exam (Aug. 1 - 15, 2025)
35%
Class Attendance
5%
Total
100%
Weekly Tentative Schedule
3
Week
#
Date
Lecture/Lab
Text
Section
Topics
Important Date
1
May 5
Lecture 1
Ch0
Introduction to Regression Analysis,
Pre-requisite Knowledge
A1 out
May 7
Lecture 2
Ch1
Pre-requisite Knowledge, Simple Linear
Regression
May 12
Lecture 3
Ch1
Least Squares Estimation, Analysis of
Variance
May 14
8.30am
May 14
Lecture 4
2
3
Ch1
May 19
4
5
Lab 1
Model Assumptions, R^2 Statistic,
Properties of Beta_0 and Beta_1
Victoria Day- no classes
May 21
Lecture 5
Ch1
Estimate sigma^2, Statistical Inference
A1 Due, A2 Out
May 26
Lecture 6
Ch4
Multiple Regression Analysis in Matrix
May 28
Lecture 7
Ch4
R^2 Statistic, Model Assumptions,
Estimate signma^2
May 28
8.30am
Lab 2
June 2
Lecture 8
Ch5
F-test for All Slopes, Inference for
Single Beta_j
June 4
Lecture 9
Ch5
Estimation and Prediction, Variance and A2 Due, A3 Out
Bias
June 9
Lecture 10
Ch6
Extra Sum of Squares Principle
A2 Due, A3 Out
4
6
June 11
Lecture 11
Ch7
The General Linear Models, Validation of
Model Assumptions
June 11
Lab 3
June 17
June 16
7
A3 Due, A4 Out
Lecture 12
Ch7
June 18
9
midterm
June 23
Lecture 13
Ch8
Interaction Effects, Residual Analysis
June 25
Lecture 14
Ch8
Residual Analysis, Leverage Points,
Standardized and Studentized
Residuals
June 25
10
11
Lab 4
June 30
Lecture 15
Ch8
Cook’s Distance, Variable Selection
Criteria
Project Out
July 2
Lecture 16
Ch13
Model Transformation, Box-cox
transformation
A4 Due, A5 Out
July 7
Lecture 17
Ch15/Ch14
Variable Selection Algorithms, Dummy
Variables
July 9
Lecture 18
Ch14
One-way ANOVA, Alternative Way
July 9
12
13
Validation of Model Assumptions,
Midterm Review
Lab 5
July 14
Lecture 19
Ch14
Two-way ANOVA
July 16
Lecture 20
Ch16
Lack of Fit Test, Collinearity and
Multicollinearity
July 21
Lecture 21
Ch12/Ch18
Polynomial Regression Model, Logistic
Regression
A5 Due
5
July 23
Lecture 22
Ch17
July 23
July28
July 30
•
Ridge and LASSO Regression
Lab 6
Lecture 23
Review
July 30-31-----Study Days
Project Due
Note: Aug. 1 - 15: Final examinations - Students are advised not to make travel commitments
during this time. There are no examinations scheduled on the Civic Holiday weekend (August 3,
4, 5).
Learning Activities, Assignments, Projects, and Examinations
1. Assignments: Assignments will be assigned approximately biweekly and equally weighted.
Usually, assignments will be posted online on Mondays, and the solutions (including both written
answers converted to PDF files and R markdown files) need to be submitted an online platform
Gradescope before the deadlines. No late assignments will be accepted, so make the effort to
submit whatever you have done by the due date. This means you should start on assignments as
soon as material is covered. Do not wait to the last minute. Data science is a collaborative
endeavour. While you may talk with others about the assignments, we ask that you write your
solutions individually in your own words. If you do discuss the assignments with others please
include their names at the top of your assignments. Keep in mind that content from assignments
will likely be covered on the exams. Assignments are your individual work!
2. Labs: There will be biweekly labs, beginning with the second week of the term (even weeks). Lab
attendance is mandatory. The biweekly labs are designed to give students practice at some
routine and some advanced problems. The goals of these sessions include helping solidify the
student's understanding of concepts in data, modern statistical tools, and introducing
applications to course topics with using computer methods for solving problems. Software used
requires minimal computer skills, but does require familiarity with R via Jupyter Notebooks. The
lab assignments are to be completed in the lab. Discussion is encouraged, but labs are to be
written by each student in an independent fashion. In preparation for each week's lab, students
are expected to have attempted all of the assigned homework problems pertaining to that
week's work and to have worked through the lab preparation for that week. It is also important
to review previous material: statistics as a discipline builds on the earlier material, and cannot be
studied effectively in pieces. The breakdown of the final lab mark will be explained during your
first lab session. Lab preparation materials, as well as solutions to lab reports will be available
through https://mylearningspace.wlu.ca.
3. Midterm: The midterm will take place in week 7 (Wednesday June 18) during the lecture time .
The midterm is a closed-book, paper-based, and proctored in-person. The location will be our
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regular classroom SB107. The midterm will test you on the materials taught in the first 6 weeks.
There will absolutely be no make-up of midterms. Weight shifting is only given to the students
with verifiable extenuating circumstances. An extenuating circumstance is a rare and severe
event for which a student has no control or cannot anticipate
(for example, a serious accident or emergent medical condition). The illness Self-Declaration
Form can be used to request academic consideration due to medical reasons, in lieu of a medical
document.
4. Projects: There will be one group project. The group size is 2. Group project will test your ability
to apply the knowledge of this course to the real world data and computing skills. Use R to
analyze some given data set to perform regression analysis. A written report is required and
should be handed in by 11:59pm sharp JULY 30 2025.
Further detailed instructions will be given in class as the project due date approaches. Should
your project be late, the projects are marked down by 10% per day, up to two days. After two
days, project submissions will not be accepted.
5. Final examination: The final exam will be a 2.5 hour and in- person exam. The date and location
will be announced later.
6. Marking: Any error in marking must be reported to your instructor within one week of the date
the work was returned in class. No marks will be changed after that time. Please see the
solutions posted on the ST562 website before bringing any alleged errors to the attention of your
instructor. The instructor reserves the right to remark the entire paper.
7. Class Attendance: Class attendance checks will be conducted randomly without notification. The
class attendance is worth 5% as part of your overall final exam. If you cannot attend the live
lectures, the weight of participation will be shifted to your final exam. However it is your
responsibility to inform the instructor about your situation at the beginning of the term.
University and Course Policies
1. Academic Calendars: Students are encouraged to review the Academic Calendar for information
regarding all important dates, deadlines, and services available on campus.
2. Accessibility: Contact Accessible Learning if you require academic accommodations because of a
disability. Review the Registration page for information about intake and documentation
requirements. Deadlines: Students are responsible for meeting posted deadlines for registering
with Accessible Learning and booking accommodated exams. Accessible Learning cannot
guarantee accommodations for requests received after posted deadlines.
3. Library Accessibility Services: The Library offers accessibility services for people with disabilities,
including alternate formats or remediation of Library collections and help accessing materials.
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For information please visit the Library Accessibility Hub (library.wlu.ca/services/accessibilityhub)
or email libaccessibility@wlu.ca.
4. The use of generative AI is permitted in this course. In all submissions in which you use
generative AI, you must cite its usage. Failing to cite the use of generative AI is a form of
academic misconduct and Senate Policy 12.2 Student Code of Conduct: Academic Misconduct
will be applied.
If you have questions about how to conduct yourself when completing course assessments,
including the use of Generative Artificial Intelligence, you should contact your course or lab
instructor for guidance. If you have general questions, please communicate with
scienceintegrity@wlu.ca.
5. Plagiarism: Wilfrid Laurier University uses software that can check for plagiarism. If requested to
do so by course instructors, students are required to submit their written work in electronic form
and have it checked for plagiarism. (Approved by Senate May 14, 2002)
6. Academic Integrity: Laurier is committed to a culture of integrity within and beyond the
classroom. This culture values trustworthiness (e.g., honesty, integrity, reliability), fairness,
caring, respect, responsibility and citizenship. Together, we have a shared responsibility to
uphold this culture in our academic and nonacademic behaviour. The University has a defined
policy with respect to academic misconduct. As a Laurier student you are responsible for
familiarizing yourself with this policy and the accompanying penalty guidelines, some of which
may appear on your transcript if there is a finding of misconduct. The relevant policy can be
found at Laurier's academic integrity website along with resources to educate and support you in
upholding a culture of integrity. Ignorance is not a defense.
7. Late Assignment Policy: No late assignments will be accepted. Should your project be late, the
projects are marked down by 10% per day, up to two days. After two days, project submissions
will not be accepted. Refer to the Handbook on Undergraduate Course Management for more
information.
8. Final Examinations: Students are strongly urged not to make any commitments (e.g., vacation)
during the examination period. Students are required to be available for examinations during the
examination periods of all terms in which they register. Refer to the Handbook on Undergraduate
Course Management for more information.
9. Religious and Spiritual Accommodation: The University welcomes students, staff and faculty
from a wide range of backgrounds, beliefs and traditions and has a duty to provide
accommodation based on creed (religion and spirituality) under the Ontario Human Rights Code.
This obligation requires the University to work with students to provide reasonable
accommodation when a student's religious observances or spiritual beliefs creates a conflict with
their academic schedule. In order for instructors to provide proper accommodations, students
have obligations to request accommodations in a timely manner. All policies, procedures,
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timelines, and request forms are found on Laurier’s Religious and Spiritual Accommodations and
Supports webpage.
10. Gender Inclusivity: This course will be conducted in an affirming and mutually respectful
atmosphere for people of all gender expressions and identities. I was provided with a class roster
with your name as it appears on the official enrollment information. If you use a name different
from the roster, please let me know at your earliest convenience. You can also share your gender
pronouns with me if you like. Members of the class are expected to refer to one another by the
name and pronouns identified by each student. If you are comfortable, you can also let your
classmates know about your name and pronouns. The Centre for Student Diversity, Equity and
Inclusion (CSEDI) has developed a website outlining how to request a different name to appear
on some university records and systems such as Zoom, MyLS and email. The website also
provides information about Laurier’s Inclusive Washroom Initiative, support resources at Laurier,
and more.
11. Classroom Use of Electronic Devices: The use of electronic devices in the classroom is governed
by WLU Policy 9.3: Classroom Use of Electronic Devices. Details of this Policy and the
consequences of breaches are stated in the Academic Calendar. Mobile devices of any kind,
including devices that emit audible signals, are not permitted in this course except for
accessibility or extenuating circumstances. The latter will be assessed on a case-by-case basis;
ask your instructor. Students who fail to comply with this policy may be asked to stow their
devices at the front of the classroom, or to leave the classroom.
12. Intellectual Property: The educational materials developed for this course, including, but not
limited to, lecture notes and slides, handout materials, examinations and assignments, and any
materials posted to MyLearningSpace, are the intellectual property of the course instructors.
These materials have been developed for student use only and they are not intended for wider
dissemination and/or communication outside of a given course. Posting or providing
unauthorized audio, video, or textual material of course content to third-party websites violates
instructors’ intellectual property rights, and the Canadian Copyright Act. Recording lectures in
any way is prohibited in this course unless specific permission has been granted by instructors.
Failure to follow these instructions may be in contravention of the university’s Student
NonAcademic Code of Conduct and/or Code of Academic Conduct, and will result in appropriate
penalties. Participation in this course constitutes an agreement by all parties to abide by the
relevant University Policies, and to respect the intellectual property of others during and after
their association with Wilfrid Laurier University.
13. Hawk Walk, the Wellness Centre, Student Supports and the Student Food Bank
Multi-campus Resource:
•
Student Rights Advisory Committee (studentsrights@wlu.ca): The Student Rights Advisory
Committee exists to provide you with information about your rights when it comes to
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landlord-tenant issues or academic appeals. While in no way legal representation, it can
help to inform you about your options to make difficult situations easier to navigate.
•
Empower Me - Mental Health Resources provided by Dialogue: Empower Me is a mental
health and wellness service that seeks to contribute to a resilient student community by
supporting existing on-campus and community mental health resources. Empower Me has a
number of professionals with various domains of expertise, including psychology,
psychotherapy, social work, nutrition, etc., to support you and respond effectively to diverse
needs. You can access services via telephone, videoconference, or in-person. Empower Me
is: available 24/7, 365 days a year, confidential, multilingual, culturally sensitive,
genderinclusive, and faith inclusive.
•
The Essentials - Legal Care Program: The Essentials, Legal Care Program allows students to
access a legal consultation service. Students are free to consult a duly certified lawyer
regarding any legal questions. Upon filling out the Support Form, students can expect a
response from legal counsel within approximately 48 hours (business days) about next steps
and assistance that is required to navigate housing disputes, employment disputes, disputes
with an academic institution, and public notaries. Students can also seek legal
representation when their case qualifies for further counsel.
Kitchener/Waterloo Resources:
•
Emergency Response Team (ert@wlu.ca): The Emergency Response Team provides medical
assistance to students on campus. ERT can be booked for on-site event support by filling out
the online booking request form on their website.
•
Waterloo Hawk Walk | 519.886.3668 | walkw@wlu.ca | Foot Patrol is a volunteer
operated safe walk-home service, available daily during evening hours. Teams of two
radiodispatched volunteers are available on request to escort students to and from campus
as well as to off-campus destinations. Foot Patrol can be found on the 2nd floor of the Fred
Nichols Campus Centre next to the Dean of Students Office.
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