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.
3. Perform statistical analyses using statistical software, incorporating underlying theory and methodologies.

Research-Led Teaching

This course builds upon the foundations students have been exposed to and mastered in introductory statistics course/s, particularly regression modelling. By adopting a research-oriented teaching framework, the contents and activities of this course is designed to enable students to build a rigorous knowledge base around specification, estimaton, and inference with generalised linear models (GLMs). Furthermore, all course contents and activities will involve a large degree of statistical computing via R and RStudio/Posit, encouraging students to develop good practical habits and an effectie statistical model-building workflow as they apply GLMs to a diverse range of real-life datasets.

All assessments are partly designed to simulate real-world research scenarios, positioning students as statistical consultants tasked with analysing an array of non-continuous data using (G)LMs. One or more assessments will also involve collaboration with other students to collect and analyze non-continuous data using GLMs, fostering peer-assisted learning and cultivating a research-oriented mindset.

Additional Course Costs

A non-programmable calculator.

Examination Material or equipment

Calculators along with a single A4 (two-sided) page of notes may be permitted, although details of this will be made available on the Wattle course webpage no later than Monday 5pm Canberra time of Week 9.

Dictionaries will not be permitted into the final exam.

Required Resources

Class material, including detailed lecture slides, recordings, tutorials, assignments, and other relevant material, will be made available on the Wattle course webpage. Students should regularly visit the site for updates and new uploads.

Recommended Resources

In the course we will make use of the R software and its RStudio interface, which are both freely available online. R must be downloaded and installed prior to downloading and installing RStudio. It is recommended to install the latest versions of these.

There are no other prescribed resources for this course: the course material made available on Wattle course webpage are designed to be self-contained and of sufficient detail to satisfy all learning outcomes of the course. A list of suggested references for optional further reading will be provided on the Wattle course webpage.

Staff Feedback

Students will be given feedback (through both verbal and written comments) via one or more of the following forms in this course:

• to the whole class during lectures;
• within tutorials;
• individually during consultation hours;
• through written comments in the marked assessments.

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.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Introduction and Revision of Linear Models (LMs)
2	Analysis of (co)variance
3	Binary regression & Poisson regression	Assignment 1 open
4	Poisson regression & introduction to Generalised linear models (GLMs)
5	Introduction to GLMs	Assignment 1 due
6	Inference and variable selection in GLMs part I
7	Inference and variable selection in GLMs II
8	Model Diagnostics	Assignment 2 open
9	Binomial regression & Multicategory regression
10	Multicategory regression & Over- and under-dispersion
11	Over- and under-dispersion	Assignment 2 due
12	Beyond GLMs (final topics to be confirmed)

Tutorial Registration

Tutorial registration using the MyTimetable function will be available two weeks prior to the beginning of the semester and will close at the end of week 1. More details can be found 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	25 %	20/03/2025	28/03/2025	1,2,3
Assignment 2	35 %	15/05/2025	26/06/2025	1,2,3
Final Exam	40 %	29/05/2025	26/06/2025	1,2,3

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

Policies

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:

• Academic Integrity Policy and Procedure
• Student Assessment (Coursework) Policy and Procedure
• Extenuating Circumstances Application
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure
• Code of practice for teaching and learning

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.

Participation

Course content delivery will take the form of weekly in-person lectures (recorded and available via Echo360 on the Wattle course webpage) in weeks 1-12, and weekly in-person tutorials in weeks 2-12, all delivered on campus.

Examination(s)

The final exam will be a centrally invigilated, two-hour closed-book exam, held in-person during the exam period. Details of the final exam will be made available on the Wattle course webpage no later than Monday 5pm Canberra time of Week 9.

As a centrally scheduled and invigilated exam, Examinations, Graduations & Prizes will timetable the final exam in a timely manner prior to the examination period. Please check ANU Timetabling for further information in due course.

Assessment Task 1

Assignment 1

The assignment will assess up to Weeks 1-4 of the course. It must be completed individually, and is not redeemable. This assignment is designed to help you reinforce the ideas and concepts behinds the application of multiple regression models as well as the basics of generalised linear models. This assignment is expected to involve a combination of mathematical/conceptual questions, and a project component involving data wrangling, visualization, and (G)LM analysis in R and RStudio.

A tentative marking rubric for the project component of Assignment 1 is provided below, noting there will be varying levels of judgement calls and marker's discretion where grading project reports. Note as part of this assessment you will be graded on aspects such as clarity and concisement of report presentaton, and choices and accuracy of data visualisation. Such aspects will not be covered in this course to any substantive degree, and it is the student's responsibility to learn and attempt these as best as possible.

Submission format: Online via the appropriate activity on the Wattle course webpage

Availability: By 5:00 pm Canberra time, Monday of Week 3

Due date: 5:00 pm Canberra Time, Thursday of Week 5

Value: 25%

Rubric

	High Distinction (5)	Distinction (4)	Credit (3)	Pass (2)	Fail (0-1)
Data visualisation and exploration	Includes accurate, well-labeled, and relevant choies of visualisations & tabulations of the data; Correct and relevant exploratory data analysis that provides initial understanding of key features of the responses and/or predictors, potential anomalies etc...	Includes accurate, well-labeled, but irrelevant visualizations & tabulations of the data; Correct but irrelevant exploratory data analysis that provides basic understanding of features of the responses and/or predictors.	Includes well-labeled, but inaccurate and irrelevant visualizations & tabulations of the data; Incorrect and irrelevant exploratory data analysis with little resulting understanding of the features of the data.	Includes visualizations & tabulations of the data that are poorly labeled, inaccurate, and irrelevant; Minimal to no exploratory data analysis.	No visualisations/tabulations of the data; No exploratory data analysis performed.
Statistical analysis and model building workflow	A correct and close to complete statistical analysis to support interpretation and conclusions; Clear and correct justifications of the steps taken in the model-building workflow, considering all aspects of inference, model diagnostics, and model selection as appropriate.	A correct but incomplete statistical analysis to support interpretation and conclusions; Clear but incorrect justifications of the steps taken in the model-building workflow, considering most aspects of inference, model diagnostics, and model selection as appropriate.	Incorrect and incomplete statistical analysis to support interpretation and conclusions; Unclear and incorrect justifications of the steps taken in the model-building workflow, considering some aspects of inference, model diagnostics, and model selection as appropriate.	Incorrect and incomplete statistical analysis with little connection to resulting interpretation and conclusions; Unclear and incorrect justifications of the steps taken in the model-building workflow with no consideration of aspects of inference, model diagnostics, and model selection as appropriate.	Little to statistical analysis done to support interpretation and conclusions; Little to no justifications of the steps taken in the model-building workflow.
Interpretation of results, presentation of conclusions, limitations/challenges, and references/acknowledgements.	Project report has all 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modeling assumptions made, and what more could have been done; Proper references and acknowledgements as appropriate.	Project report has 3 of the 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modeling assumptions made, and what more could have been done; Proper references and acknowledgements as appropriate.	Project report has 2 of the 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modeling assumptions made, and what more could have been done; Proper references and acknowledgements as appropriate.	Project report has 1 of the 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modeling assumptions made, and what more could have been done; Proper references and acknowledgements as appropriate.	Project report has none of the 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modeling assumptions made, and what more could have been done; Proper references and acknowledgements as appropriate.
Overall presentation	Project report has all 4 qualities: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Well-written with little to no grammatical and spelling errors; Clear structure including statement of research question/s.	Project report has 3 of the 4 qualities: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Little to no grammatical and spelling errors; Clear structure including statement of research question/s.	Project report has 2 of the 4 qualities: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Little to no grammatical and spelling errors; Clear structure including statement of research question/s.	Project report has 1 of the 4 qualities: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Little to no grammatical and spelling errors; Clear structure including statement of research question/s.	Project report has none of the 4 qualties: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Little to no grammatical and spelling errors; Clear structure including statement of research question/s.

Assessment Task 2

Assignment 2

The assignment will assess Weeks 1-10 of the course. It will involve a combinaton of individual work, along with a component that will be a group project (designed such that all students will be provided with the opportunity to demonstrate their skills as individuals). The assignment is not redeemable. This assignment is designed to help you understand and reinforce many of the elements of specifying, fitting, and performing inference using generalised linear models models, and is expected to involve a combination of mathematical/conceptual questions, while the group project component will involve data wrangling, visualization, and (G)LM analysis in R and RStudio.

A tentative marking rubric for the group project component of Assignment 2 is provided below, noting there will be varying levels of judgement calls and marker's discretion where grading project reports. Note as part of this assessment you will be graded on aspects such as mode of study design and data collection, clarity and concisement of report presentaton, and choices and accuracy of data visualisation. Such aspects will not be covered in this course to any substantive degree, and it is the student's responsibility to learn and attempt these as best as possible.

Submission format: Online via the appropriate activity on the Wattle course webpage

Availability: By 5:00 pm Canberra time, Monday of Week 8

Due date: 5:00 pm Canberra Time, Thursday of Week 11

Value: 35%

Rubric

	High Distinction (5)	Distinction (4)	Credit (3)	Pass (2)	Fail (0-1)
Data collection and description	Thorough description of how the data were collected, along with demonstrable evidence of thought given into the study design; A concise summary of the variables recorded, with critical information such as roles in the model (response versus covariate), variable type (e.g, binary/ordinal factor or continuous, range of values it can take), relevant to research question of interest, all clearly provided.	Thorough description of how the data were collected, but little evidence of thought given into the study design; A concise summary of the variables selected, with only some critical information such as roles in the model (response versus covariate), variable type (e.g, binary/ordinal factor or continuous, range of values it can take), relevant to research question of interest, provided.	Minimal description of how the data were collected, with little evidence of thought given into the study design; A summary of the variables selected, with only basic information such as roles in the model (response versus covariate) provided.	Minmial description of how the data were collected, with no evidence of thought given into the study design; Only a basic itemisation of the variables recorded.	No description of how the data were collected, or of study design; No summary of the variables recorded
Data wrangling, visualisation, and exploration	Includes accurate, well-labeled, and relevant choies of visualisations & tabulations of the data; Correct and relevant details of data wrangling performed, along with exploratory data analysis that provides initial understanding of key features of the responses and/or predictors, potential anomalies etc...	Includes accurate, well-labeled, but irrelevant visualizations & tabulations of the data; Correct but irrelevant details of data wrangling performed and/or exploratory data analysis that provides basic understanding of features of the responses and/or predictors.	Includes well-labeled, but inaccurate and irrelevant visualizations & tabulations of the data; Incorrect and irrelevant data wrangling and/lor exploratory data analysis with little resulting understanding of the features of the data.	Includes visualizations & tabulations of the data that are poorly labeled, inaccurate, and irrelevant; Minimal to no discussion of data wrangling and exploratory data analysis.	No visualisations/tabulations of the data; No data wrangling and exploratory data analysis performed.
Statistical analysis and model building workflow	A correct and close to complete statistical analysis to support interpretation and conclusions; Clear and correct justifications of the steps taken in the model-building workflow, considering all aspects of inference, model diagnostics, and model selection as appropriate.	A correct but incomplete statistical analysis to support interpretation and conclusions; Clear but incorrect justifications of the steps taken in the model-building workflow, considering most aspects of inference, model diagnostics, and model selection as appropriate.	Incorrect and incomplete statistical analysis to support interpretation and conclusions; Unclear and incorrect justifications of the steps taken in the model-building workflow, considering some aspects of inference, model diagnostics, and model selection as appropriate.	Incorrect and incomplete statistical analysis with little connection to resulting interpretation and conclusions; Unclear and incorrect justifications of the steps taken in the model-building workflow with no consideration of aspects of inference, model diagnostics, and model selection as appropriate.	Little to statistical analysis done to support interpretation and conclusions; Little to no justifications of the steps taken in the model-building workflow.
Interpretation of results, presentation of conclusions, limitations/challenges, and references/acknowledgements.	Project report has all 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modelling assumptions made, and what more could have been done both in analysis and in data collection/experimental design; Proper references and acknowledgements as appropriate, including statement of contributions of each individual to the project.	Project report has 3 of the 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modelling assumptions made, and what more could have been done both in analysis and in data collection/experimental design; Proper references and acknowledgements as appropriate, including statement of contributions of each individual to the project.	Project report has 2 of the 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modelling assumptions made, and what more could have been done both in analysis and in data collection/experimental design; Proper references and acknowledgements as appropriate, including statement of contributions of each individual to the project.	Project report has 1 of the 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modelling assumptions made, and what more could have been done both in analysis and in data collection/experimental design; Proper references and acknowledgements as appropriate, including statement of contributions of each individual to the project.	Project report has 0 of the 4 qualities: Substantive and correct interpretation of modeling results; A succinct summary of the main conclusions of the analysis; Discussion around limitations of analysis, modelling assumptions made, and what more could have been done both in analysis and in data collection/experimental design; Proper references and acknowledgements as appropriate, including statement of contributions of each individual to the project.
Overall presentation	Project report has all 4 qualities: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Little to no grammatical and spelling errors; Clear structure including statement of research question/s.	Project report has 3 of the 4 qualities: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Little to no grammatical and spelling errors; Clear structure including statement of research question/s.	Project report has 2 of the 4 qualities: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Little to no grammatical and spelling errors; Clear structure including statement of research question/s.	Project report has 1 of the 4 qualities: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Little to no grammatical and spelling errors; Clear structure including statement of research question/s.	Project report has none of the 4 qualities: Visually appealing and consistent formatting; Clear and concise writing style, using statistical jargon only as necessary; Little to no grammatical and spelling errors; Clear structure including statement of research question/s.

Assessment Task 3

Final Exam

The final exam will be a centrally invigilated, two-hour closed-book exam, held in-person during the exam period. Details of the final exam will be made available on the Wattle course webpage no later than Monday 5pm Canberra time of Week 9.

As a centrally scheduled and invigilated exam, Examinations, Graduations & Prizes will timetable the final exam in a timely manner prior to the examination period. Please check ANU Timetabling for further information in due course.

The final exam must be completed individually, is not redeemable, and is not a hurdle assessment. The exam will assess all weeks of the course, and will consist of up to four questions, each with multiple parts, and will be at a level similar to that of the Assessment Tasks 1 and 2.

Value: 40%

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 will be required to sign a declaration, in the form of a cover sheet, as part of the submission of Assignments 1 and 2. Please keep a copy of the assignment and signed cover sheets, for your record. Any student identified, either during the current semester or in retrospect, as having used ghost writing services as part of the Assignments 1 and 2 will be investigated under the University’s Academic Misconduct Rule.

Each assignment must be submitted as one or more electronic files via the appropriate activity on the Wattle course webpage, preferably a combination of pdf and R software scripts (as appropriate), to the appropriate activity on the Wattle course webpage. If submitting handwritten mathematical derivations, ensure that your handwriting is legible, appropriate working out is shown, and then scan the derivations in e.g., by using your smartphone camera and Microsoft Lens.

More information about online submissions of each assessments task will be given on the course Wattle course webpage, in a timely manner,

Hardcopy Submission

There is no hardcopy submission in the course.

Late Submission

No submission of any assessment tasks is permitted. If an assessment task is submitted after the due date, a mark of zero 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. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

Returning Assignments

Graded assignments along with feedback should be made available via the relevant activity on the course Wattle site at an appropriate time.

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

Resubmission of assessments is not allowed under any circumstance.

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).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Accessibility for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents all ANU students