• Class Number 7247
  • Term Code 3260
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Prof Andrew Wood
    • Prof Andrew Wood
  • Class Dates
  • Class Start Date 25/07/2022
  • Class End Date 28/10/2022
  • Census Date 31/08/2022
  • Last Date to Enrol 01/08/2022
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 analysing 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.

Learning Outcomes

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

  1. Explain in detail the role of generalised linear modelling techniques (GLMs) in modern applied statistics and implement methodology;
  2. Demonstrate an in-depth understanding of the underlying assumptions for GLMs and perform diagnostic checks whilst identifying potential problems; and
  3. Perform statistical analyses using statistical software, incorporating underlying theory and methodologies.

Research-Led Teaching

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

Field Trips

Not relevant in this course.

Examination Material or equipment

You will require reliable access to Wattle and a calculator for the duration of the online quizzes and the online exam. You may use R for numerical calculations if you wish.

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 (which is shared with the co-taught courses, STAT3015 and STAT4030). 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 some 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
  • verbal comments (e.g. during live workshops/tutorials, and during online office hours).
  • feedback to the whole class, groups, individuals, focus group etc.

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

Enrollment and Prerequisites

This is 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 statistical computing with R and multiple regression Weekly tutorials commence
2 Analysis of variance (ANOVA) models. Random effects models.
3 Analysis of covariance (ANCOVA) models Quiz 1 via Wattle; Assignment 1 to be released
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
6 Parameter estimation and interpretation Quiz 2 via Wattle
7 Analysis of deviance and residual diagnostics
8 Variable selection for GLMs
9 Modelling binomial proportions and Poisson counts Quiz 3 via Wattle; Assignment 2 to be released
10 Over-dispersion and under-dispersion
11 Odds ratios and contingency tables Assignment 2 due
12 More on contingency tables Quiz 4 via Wattle

Tutorial Registration

Tutorials will be available on campus (in person), live through scheduled Zoom sessions and as pre-recorded videos. 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
Wattle Quiz 1 5 % 12/08/2022 19/08/2022 1, 2, 3
Assignment 1 (Linear models) 15 % 26/08/2022 02/09/2022 1, 2, 3
Wattle Quiz 2 5 % 02/09/2022 02/09/2022 1, 2, 3
Wattle Quiz 3 5 % 07/10/2022 07/10/2022 1, 2, 3
Assignment 2 (GLMs) 15 % 21/10/2022 28/10/2022 1, 2, 3
Wattle Quiz 4 5 % 28/10/2022 28/10/2022 1, 2, 3
Final Examination 50 % 03/11/2022 01/12/2022 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 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 Academic Integrity . 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 typically be student-led rather than lecturer-led and will provide plenty of opportunity to ask questions about and discuss the contents of the course.


The exam 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: 5 %
Due Date: 12/08/2022
Return of Assessment: 19/08/2022
Learning Outcomes: 1, 2, 3

Wattle Quiz 1

This assessment will consist of a quiz on Wattle consisting of 10 multiple choice questions to be completed in 1 hour. Each question will have four answers, only one of which will be correct, and there is no penalty for an incorrect answer (other than scoring 0 for that question). The quiz must be done individually and be completed sometime between 12 noon Canberra time on Monday 8 August 2022 and before 3pm Canberra time on Friday 12 August 2022. Further details to follow on the Wattle course page.

Assessment Task 2

Value: 15 %
Due Date: 26/08/2022
Return of Assessment: 02/09/2022
Learning Outcomes: 1, 2, 3

Assignment 1 (Linear models)

Assignment 1 (=Assessment 2) will be made available on Wattle before 3pm on Friday 12 August 2022 and will be due before 3pm on Friday 26 August 2022. The assignment may be done individually or in groups of up to 3 students and will involve using R to analyse data from a case study, then editing the R output and preparing a written report on your results. Assignment 1 is worth 15% 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). Options for producing this pdf file are: (i) using a text processing package such as latex to produce a pdf document; or (ii) scanning hand-written solutions into a pdf document; or (iii) photographing hand-written solutions into a pdf document. If you use options (ii) or (iii), please ensure that your handwriting is legible. Assignments will be graded and marks will be returned via Wattle.

Assessment Task 3

Value: 5 %
Due Date: 02/09/2022
Return of Assessment: 02/09/2022
Learning Outcomes: 1, 2, 3

Wattle Quiz 2

This assessment will consist of a Wattle quiz with the same structure as that of Assessment 1 (=Quiz 1). The quiz must be done individually and be completed sometime between 12 noon Canberra time on Monday 29 August 2022 and before 3pm Canberra time on Friday 2 September 2022. Further details to follow on the Wattle course page.

Assessment Task 4

Value: 5 %
Due Date: 07/10/2022
Return of Assessment: 07/10/2022
Learning Outcomes: 1, 2, 3

Wattle Quiz 3

This assessment will consist of a Wattle quiz with the same structure as that of Assessment 1 (=Quiz 1). The quiz must be done individually and be completed sometime between 12 noon Canberra time on Monday 3 October 2022 and before 3pm Canberra time on Friday 7 October 2022. Further details to follow on the Wattle course page.

Assessment Task 5

Value: 15 %
Due Date: 21/10/2022
Return of Assessment: 28/10/2022
Learning Outcomes: 1, 2, 3

Assignment 2 (GLMs)

Assignment 2 (=Assessment 5) will be handed out not later than 3pm Canberra time on Friday 7 October 2022 will be due in no later than 3pm Canberra time on Friday 21 October 2022 . Details are otherwise the same as for Assignment 1 (=Assessment 2).

Assessment Task 6

Value: 5 %
Due Date: 28/10/2022
Return of Assessment: 28/10/2022
Learning Outcomes: 1, 2, 3

Wattle Quiz 4

This assessment will consist of a Wattle quiz with the same structure as that of Assessment 1 (=Quiz 1). The quiz must be done individually and be completed sometime between 12 noon Canberra time on Monday 24 October 2022 and before 3pm Canberra time on Friday 28 October 2022. Further details to follow on the Wattle course page.

Assessment Task 7

Value: 50 %
Due Date: 03/11/2022
Return of Assessment: 01/12/2022
Learning Outcomes: 1, 2, 3

Final Examination

The final examination will be a Wattle-based online exam during the university examination period at the end of the semester. The exam will be 2 hours long (plus 15 minutes reading time plus 15 minutes online submission time) and will cover the entire syllabus. It will be open book and all materials are permitted. 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. 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 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. 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

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

Assignments will be marked to a commonly agreed marking schedule by your tutor and returned to you online or in tutorials.

Assignment solutions will be discussed in the tutorials in the first teaching week following the due date.

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

Wednesday 16:00 17:00
Wednesday 16:00 17:00
Prof Andrew Wood
6125 7373

Research Interests

Prof Andrew Wood

Wednesday 16:00 17:00
Wednesday 16:00 17:00

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