• Class Number 7470
  • Term Code 2960
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Ian McDermid
    • Ian McDermid
  • Class Dates
  • Class Start Date 22/07/2019
  • Class End Date 25/10/2019
  • Census Date 31/08/2019
  • Last Date to Enrol 29/07/2019
SELT Survey Results

Course Description: 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. 

The R statistical computing package is used as an integral part of the course.

For students enrolled in STAT7030, there may be alternative assignment or examination problems.

Learning Outcomes

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

Upon successful completion of the requirements for this course, students should 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; appreciate potential problems.
  3. Perform statistical analysis using statistical software in addition to learning the underlying theory and methodologies.

Research-Led Teaching

My teaching in this course about statistical modelling will draw on numerous examples from my extensive experience in applied statistical research and consulting.

Field Trips

Not relevant in this course.

Examination Material or equipment

No electronic aids are permitted (e.g. laptops, phones), but you will be provided with a calculator (HP Scientific Calculator 300s+) for the final examination

Unannotated paper-based dictionary (no approval required).

One A4 page with hand-written notes on both sides.

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, STAT4030 and STAT7030). It is essential that you visit the class Wattle site regularly, but 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 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. All tutorials for this course will be held as Computer Laboratories in one of the InfoCommons PC laboratories, though you may find it helpful to also bring a laptop with R installed to the tutorials.

As we have a lot of detailed course material already available, there is NO prescribed text for this course. However, I will post on Wattle a list of recommended references for supplementary reading and may also make additional material available in the library e-Reserve.

Staff Feedback

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

  • individual feedback will be provided by your tutor, who will mark your assignments. Solutions to the assignments will be provided on Wattle and discussed in tutorials and/or lectures.
  • you are also welcome to ask questions of me or your tutor at consultations or during classes. If you wish to ask me questions immediately following a class, please wait for me outside the classroom, so that I can clean-up and log-off in preparation for the next class that will be using the same venue.
  • I am also happy to answer short questions on the course material sent via email or posted on the discussion forum on Wattle. If you send me a question via email, I will (unless you specifically ask me not to) post your question (anonymously) and my answer on Wattle for the benefit of all students.

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
4 Modelling binary data with logistic regression. Introduction to GLMs.
5 Model specification, link functions and likelihood inference for GLMs Assignment 1 due
6 Parameter estimation and interpretation MS Quiz via Wattle
7 Analysis of deviance and residual diagnostics
8 Variable selection for GLMs
9 Modelling binomial proportions and counts
10 Poisson and gamma GLMs
11 Over and under-dispersion Assignment 2 due
12 Odds ratios and contingency counts

Tutorial Registration

Tutorial registration will be via the course Wattle site, where details of tutors and consulting hours will also be posted.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 (ANOVA and ANCOVA models) 15 % 23/08/2019 30/08/2019 1, 2, 3
MS Wattle Quiz 5 % 30/08/2019 30/08/2019 1, 2, 3
Assignment 2 (GLMs) 20 % 18/10/2019 25/10/2019 1, 2, 3
Final Examination 60 % 31/10/2019 28/11/2019 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 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.


Will not be assessed, but attendance and participation, especially at the workshops and tutorials (computer labs) is strongly recommended.


See assessment task 4, above. Centrally administered examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information.

Assessment Task 1

Value: 15 %
Due Date: 23/08/2019
Return of Assessment: 30/08/2019
Learning Outcomes: 1, 2, 3

Assignment 1 (ANOVA and ANCOVA models)

Detailed assignment specifications will be handed out at least three weeks prior to the due date. Assignment may be done individually or in groups and will definitely involve using R to analyze data from a case study, then editing the R output and preparing a written report on your analyses.

Worth 15% of the overall assessment. Note that this is an applied statistics course and the assignments represent an opportunity for you to show that you can correctly apply the statistical techniques. So this assignment is compulsory and the marks are NOT redeemable on the final exam.

Assessment Task 2

Value: 5 %
Due Date: 30/08/2019
Return of Assessment: 30/08/2019
Learning Outcomes: 1, 2, 3

MS Wattle Quiz

An (optional) short quiz will be made available on Wattle for you to complete in week 6, closing at 12 noon on Friday, 30 August 2019.

Worth 5% of the overall assessment, however, marks for this quiz will be redeemable on Assignment 1 (which will be worth 20%, if your marks for Assignment 1 are better

than your results on the quiz).

Assessment Task 3

Value: 20 %
Due Date: 18/10/2019
Return of Assessment: 25/10/2019
Learning Outcomes: 1, 2, 3

Assignment 2 (GLMs)

Detailed assignment specifications will be handed out at least three weeks prior to the due date. Assignment may be done individually or in groups and will definitely involve using R to analyze data from a case study, then editing the R output and preparing a written report on your analyses.

Worth 20% of the overall assessment. Note that this is an applied statistics course and the assignments represent an opportunity for you to show that you can correctly apply the statistical techniques. So this assignment is compulsory and the marks are NOT redeemable on the final exam.

Assessment Task 4

Value: 60 %
Due Date: 31/10/2019
Return of Assessment: 28/11/2019
Learning Outcomes: 1, 2, 3

Final Examination

3 hour formal examination to be scheduled during the end of semester exam period.

Worth 60% of the overall assessment.

You will be provided with a calculator (HP Scientific Calculator 300s+) for this exam. Only calculators provided by the Examinations Office on the day of the exam are permitted in the exam room

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

You may choose not to submit your assignments through Turnitin. If you choose this option, you will be required to submit your assignment report (and copies of all references included in the assignment report) in hard copy by the due date. All submitted hard copy assignments must include a completed a completed cover sheet clearly identifying all members of your group. Please keep a copy of the assignment for your records.

Late Submission

No submission of assessment tasks without an extension after the due date will be permitted.

If an assessment task is not submitted by the due date, 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. The Course Convener may grant extensions 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).

Ian McDermid

Research Interests

Over 30 years experience in statistical consulting, research and teaching. My current research interests are in: population health and mortality; sample survey analysis and design.

Ian McDermid

Ian McDermid

Research Interests

Ian McDermid

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