• Class Number 3571
  • Term Code 3140
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Linh Nghiem
    • Linh Nghiem
  • Class Dates
  • Class Start Date 31/05/2021
  • Class End Date 30/07/2021
  • Census Date 11/06/2021
  • Last Date to Enrol 11/06/2021
    • Wei Li
SELT Survey Results

Regression Modelling is a course in applied statistics that studies the use of linear regression techniques for examining relationships between variables. The course emphasises the principles of statistical modelling through the iterative process of fitting a model, examining the fit to assess imperfections in the model and suggest alternative models, and continuing until a satisfactory model is reached. Both steps in this process require the use of a computer: model fitting uses various numerical algorithms, and model assessment involves extensive use of graphical displays. The R statistical computing package is used as an integral part of the course.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Demonstrate a thorough understanding of the R statistical computing language, particularly the graphical capabilities;
  2. Fit simple linear regression models, interpret model parameters and relate theses back to the underlying research question;
  3. Analyse and interpret relationships between a response variable and a covariate;
  4. Analyse and interpret relationships between a response variable and several covariates;
  5. Assess and refine simple and multiple linear regression models based on diagnostic measures, including identifying and discuss the implications of outlying and influential data points; and,
  6. Select and discuss a useful multiple linear regression model from a number of competing models.

Research-Led Teaching

My teaching in this introductory course in statistical modeling will draw on examples from my experience in statistical research and consulting.

Required Resources

There is no required textbook for the class. All the lecture notes, required reading, and additional resources will be posted on Wattle.

  • Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models.
  • Faraway, J. J. (2014). Linear models with R. CRC press.

Staff Feedback

You will be given individual feedback for each assignment. Solutions to the assignments will be provided on Wattle. Additionally, general verbal comments will be provided to the whole class.

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Other Information

Course materials sharing is prohibited

Any materials in this class may not be shared online or with anyone outside of the class unless you have the convener's explicit, written permission. This includes but is not limited to lecture videos, assessments, in-class materials, review sheets, and additional problem sets.

Support for Students

The University offers a number of support services for students. Information on these is available online from http : //students.anu.edu.au/studentlife/

Communication via Email

If I, or anyone in the School, College or University administration, need to contact you, we will do so via your official ANU student email address, which you need to check regularly. If you have any questions for the teaching and course convenor make sure you email them using your ANU email address. Emails from personal email accounts will not be answered.


Students are expected to check the Wattle site for announcements about this course, e.g. changes to timetables or notifications of cancellations.

Assessment Requirements

Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule.


Your final mark for the course will be based on the raw marks allocated for each of your assessment items. However, your final mark may not be the same number as produced by that formula, as marks may be scaled. Any scaling applied will preserve the rank order of raw marks (i.e. if your raw mark exceeds that of another student, then your scaled mark will exceed the scaled mark of that student), and may be either up or down.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction and review of basic statistical concepts. Introduction to R. (optional)
2 Overview of regression analysis and linear model.
3 Simple linear regression model (SLR): model descriptions, assumptions, and model fitting through ordinary least squares (OLS). Properties of estimators.
4 SLR: Inference and prediction. Analysis of variance.
5 SLR: Quality of fitted model and basic model diagnostics. Assignment 1
6 Basic matrix and linear algebra.
7 Multiple linear regression model (MLR): model descriptions, assumptions, and model fitting through OLS. Wattle Quiz
8 MLR: Properties of OLS estimators. Inference and prediction.
9 MLR: Multicollinearity. Models with qualitative variables and interactions.
10 MLR: Variable selection and model selection.
11 MLR: Quality of fitted models and model diagnostics.
12 Weighted least squares and robust regression.
13 More on multicollinearity: principal component regression and ridge regression. Assignment 2, Final Exam

Tutorial Registration

Tutorial registration will not be required.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 (Simple Linear Regression) 25 % 13/06/2021 20/06/2021 1-3
Wattle Quiz 10 % 27/06/2021 04/07/2021 4-6
Assignment 2 (Multiple Regression) 35 % 16/07/2021 23/07/2021 4-6
Final exam 30 % 30/07/2021 * 1-6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.


The course will begin on Monday, 31 May, and the intensive week is from 28 June to 2 July.

The pre- and post-intensive period will contain pre-recorded lectures and tutorials. During the one-week intensive period, the course will operate in dual-delivery format (on-campus as well as live online (recorded) lectures and tutorials). Tentatively, each day of the intensive week will consists of 1 x 3.5 hour live lectures, plus 1 x 2 hour live tutorial. All lectures and tutorials will be streamed live via Zoom, as well as recorded.


Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule.

Assessment Task 1

Value: 25 %
Due Date: 13/06/2021
Return of Assessment: 20/06/2021
Learning Outcomes: 1-3

Assignment 1 (Simple Linear Regression)

This assignment is designed to help you understand the simple linear regression model. This assignment involves both mathematical/conceptual questions (50%) and data analysis in R (50%). It will be released no later than two weeks before the due date, is completed individually, and not redeemable.

Assessment Task 2

Value: 10 %
Due Date: 27/06/2021
Return of Assessment: 04/07/2021
Learning Outcomes: 4-6

Wattle Quiz

The quiz (using Wattle quiz tool) is designed to help you review linear algebra and the matrix formulation of multiple linear regression models. It will be released no later than two weeks before the due date, is completed individually, and not redeemable. Each student will have only one attempt (before the deadline), but the attempt is not timed (i.e student can take as much time as needed).

Assessment Task 3

Value: 35 %
Due Date: 16/07/2021
Return of Assessment: 23/07/2021
Learning Outcomes: 4-6

Assignment 2 (Multiple Regression)

This assignment is designed to help you understand multiple regression models. It contains two main parts: part 1 (50%) involves mathematical/conceptual questions and analyses of model outputs, part 2 (50%) is a mini-project that requires you to analyze real datasets. For this assignment, you are encouraged to work in groups of no more than three students, although you can work individually. This assignment is not redeemable.

Assessment Task 4

Value: 30 %
Due Date: 30/07/2021
Learning Outcomes: 1-6

Final exam

Details of the final take-home will be announced on Wattle no later than the end of the intensive week (28 June - 2 July).

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.

The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.

The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

Hard copy submissions will not be available.

Late Submission

Late submissions will not be accepted.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Returning Assignments

Assignment will be returned online.


Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Assignments may not be resubmitted.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Linh Nghiem

Research Interests

measurement error modelling, dimension reduction, graphical model, Bayesian methodology

Linh Nghiem

Tuesday 16:00 18:00
Tuesday 16:00 18:00
By Appointment
Linh Nghiem
02 61250487

Research Interests

Linh Nghiem

Tuesday 16:00 18:00
Tuesday 16:00 18:00
By Appointment
Wei Li

Research Interests

Wei Li

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions