• Class Number 6983
  • Term Code 3360
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Prof Andrew Wood
    • Prof Andrew Wood
  • Class Dates
  • Class Start Date 24/07/2023
  • Class End Date 27/10/2023
  • Census Date 31/08/2023
  • Last Date to Enrol 31/07/2023
SELT Survey Results

This course is intended to introduce students to generalised linear modelling methods, with emphasis on, but not limited to, common methods for analyzing categorical data. Topics covered include a review of multiple linear regression and the analysis of variance, log-linear models for contingency tables, logistic regression for binary response data, Poisson regression, model selection and model checking, mixed effects models. Additional topics may include Bayesian analysis for generalized linear models and generalized mixed effect models.

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. Communicate the role of generalised linear modelling techniques (GLMs) in modern applied statistics and implement methodology.
  2. Explain the underlying assumptions for GLMs and perform diagnostic checks whilst identifying potential problems.
  3. Perform statistical analysis using statistical software, incorporating underlying theory and methodologies.

Research-Led Teaching

The teaching in this course about statistical modelling will draw on the lecturer's extensive research experience in developing statistical methodology, doing applied statistical research and statistical consulting.

Field Trips

Not relevant in this course.

Examination Material or equipment

You will require a non-programable hand calculator for the end-of-semester exam.

Required Resources

Class materials, including detailed lecture notes, slides, lecture demonstrations, tutorials, assignments and other relevant materials, will be made available on the class web page on Wattle. It is essential that you visit the class Wattle site regularly. Important: you will need to be correctly enrolled in the course before you can access the Wattle site.

The application of modern statistical techniques requires familiarity with some statistical computing package and the larger assignments for this course will require statistical analysis on a computer. This course makes extensive use of the R computing package, which is freely available to download at http://www.r-project.org. Further instructions on R, including a series of revision workshops, will be made available on the Wattle site for this course. R is also available on all InfoCommons computers on the ANU campus.

As there is already a lot of detailed course material already available, and the course lecture notes are designed to be self-contained, there is NO prescribed text for this course. However, I will post on Wattle a list of suggested references for optional supplementary reading.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments (e.g. a summary on Wattle of class performance in each assignment)
  • verbal comments (e.g. during live workshops/tutorials)
  • individual verbal comments upon request (e.g. during office hours)

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). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Other Information

Enrollment and Prerequisites

This is principally a course in applied statistics, using numerous examples, rather than a course in mathematical statistics; but it is NOT an introductory first course in either statistical modelling or basic statistics. We assume you have already completed a course such as STAT2008 Regression Modelling as an essential prerequisite AND that you have also completed the equivalent of an introductory course in basic statistics (such as STAT1003 or STAT1008) that is an essential prerequisite to the STAT2008 course.

The course also uses the R statistical package, which applies matrix algebra to implement the linear modelling techniques. An understanding of matrix algebra (equivalent to an

introductory mathematics course such as MATH1113) would be helpful in understanding how the R routines work, but such knowledge is not a required prerequisite nor an

examinable part of this course.


If moderation of marks is required, then marks may be scaled. 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, if marks are scaled. Any scaling applied will be restricted to your course code (not across different co-taught courses) and 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 or be the same as the scaled mark of that student), and may be either up or down.

Communication and Announcements

If the course instructors 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. Information from the Registrar and Student Services’ office will also be sent to this email address.

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

Class Schedule

Week/Session Summary of Activities Assessment
1 Revision of multiple regression
2 Analysis of variance (ANOVA) models. Weekly tutorials commence
3 Analysis of covariance (ANCOVA) models. Random effects models. Assignment 1 to be released on 11 August 2023 before 5pm
4 Introduction to GLMs. Key GLMs: normal, logistic and Poisson regression models
5 Model specification, link functions and likelihood inference for GLMs Assignment 1 due on 25 August 2023 before 5pm
6 Parameter estimation and interpretation
7 Analysis of deviance and residual diagnostics
8 Variable selection for GLMs
9 Modelling binomial proportions and Poisson counts Assignment 2 to be released on 6 October 2023 before 5pm
10 Over-dispersion and under-dispersion
11 Odds ratios and contingency tables Assignment 2 due on 20 October 2023 before 5pm
12 More on contingency tables

Tutorial Registration

Tutorials will be available on campus in person. ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage (https://www.anu.edu.au/students/program-administration/timetabling).

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 (Linear models) 25 % 25/08/2023 31/08/2023 1, 2, 3
Assignment 2 (GLMs) 25 % 20/10/2023 31/10/2023 1, 2, 3
Final Examination 50 % 02/11/2023 30/11/2023 1, 2, 3

* 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 Integrity 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 Academic Skills 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.


Course content delivery will take the form of 3 hours of in-person lectures per week which will be recorded and subsequently made available via Echo360 on Wattle. In addition there will be a one hour workshop per week on campus (in person) which will be recorded and subsequently made available via Echo360 on Wattle. The workshops will often be student-led rather than lecturer-led and will provide plenty of opportunity for students to ask questions about and discuss the contents of the course. Attendance and participation in lectures, workshops and tutorials is recommended but is not assessed.


The end-of-semester examination will be centrally scheduled through Examinations, Graduations & Prizes and will be timetabled prior to the examination period. Please check ANU Timetabling for further information.

Assessment Task 1

Value: 25 %
Due Date: 25/08/2023
Return of Assessment: 31/08/2023
Learning Outcomes: 1, 2, 3

Assignment 1 (Linear models)

Assignment 1 (=Assessment 1) will be made available on Wattle before 5pm on Friday 11 August 2023 and will be due before 5pm on Friday 25 August 2023. Assignment 1 will consist of performing a statistical analysis on one or more datasets using R and then editing the R output and writing a report summarising and presenting the results. You will also be asked to write a non-technical summary of your findings. The assignment may be done individually or in groups of up to 3 students. Assignment 1 is worth 25% of the overall assessment. When completed, solutions to Assignment 1 (including the declaration sheet) should be submitted to Wattle in a single pdf file (details of how to do this will be given on Wattle). This pdf file should be produced using a text processing package such as latex or word to produce a pdf document.

Assessment Task 2

Value: 25 %
Due Date: 20/10/2023
Return of Assessment: 31/10/2023
Learning Outcomes: 1, 2, 3

Assignment 2 (GLMs)

Assignment 2 (= Assessment 2) will be handed out not later than 5pm Canberra time on Friday 6 October 2023 and will be due in no later than 5pm Canberra time on Friday 20 October 2023 . Details are otherwise similar to those for Assignment 1 (=Assessment 1).

Assessment Task 3

Value: 50 %
Due Date: 02/11/2023
Return of Assessment: 30/11/2023
Learning Outcomes: 1, 2, 3

Final Examination

The final examination will be an in-person exam that will take place during the university examination period at the end of the semester. The exam will be 2 hours long (plus 15 minutes reading time) and will cover the entire syllabus minus any topics that are explicitly excluded by the lecturer. The exam will be centrally timetabled and details of the final examination timetable will be made available on the ANU Timetabling website.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You are encouraged to submit assignments online through Turnitin, which will require you to electronically sign a declaration as part of the submission of your assignment. Assignments may be submitted by just one member of the group, but must include a completed cover sheet clearly identifying all members of the group. For those who work in a group of at least two people, a brief summary of the contributions of each individual should be provided and signed by each member of the group. This additional sheet will not form part of the page limit. Please keep a copy of the assignment for your records.

Hardcopy Submission

Online submission only.

Late Submission

If an extension is not obtained from the Course Convenor before the due date for submission, submission after the due date will not be permitted.

If an assessment task is not submitted by the due date, and no extension has been granted by the Course Convenor, a mark of 0 will be awarded.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Returning Assignments

Assignments will be marked by your tutor, overseen by the module convenor, to a commonly agreed marking schedule. The results will be returned to you 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

There will be no resubmission of assignments.

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).

Prof Andrew Wood

Research Interests

Statistical Analysis and Modelling (including the use Generalised Linear Models), Computational Statistics, Statistical Theory. Application areas: Biology, Earth Sciences, Medicine.

Prof Andrew Wood

Thursday 16:15 17:15
Thursday 16:15 17:15
Prof Andrew Wood
6125 7373

Research Interests

Prof Andrew Wood

Thursday 16:15 17:15
Thursday 16:15 17:15

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