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 elaborates as well as builds upon the statistical principles to which you have been exposed in introductory statistics course/s, particularly regression modelling. The contents and activities in this course are designed to help you to build a more rigorous yet data-driven foundation towards a better understanding of generalized linear models, and the work ow behind good statistical model-building using GLMs for non-continuous response data. Course contents and activities will involve statistical computing with R interfaced through R Studio. Additionally, one or more of the assessment tasks will involve the statistical analysis of datasets using linear and generalized linear models, mimicking real-life applied statistics and placing the student as the researcher and "statistical consultant".

Additional Course Costs

Optional purchase of a non-programmable calculator.

Examination Material or equipment

The nal exam will be a take-home exam. It will be an open-book exam and you will be able to go online and use probability tables or use statistical software such as R, as appropriate. However, copying, cheating, collaboration and collusion, ghost writing, fabrication, plagarism, answer recycling, and all use of social media, tutoring websites such as Chegg, and large language models and chatbots such as ChatGPT and Google Bard are not permitted. Please see the Examination section below for more information. Final details of the take home exam, along with all other assessments, will be made available on the Wattle page no later than 5pm on the Fridat of the intensive week.

Required Resources

Class material, including detailed lecture notes, slides, lecture demonstrations, tutorials, assignments and other relevant material, will be made available on the class web page on Wattle. Students should regularly visit the Wattle site of the course.

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 as a lot of detailed course material is available on Wattle and the course lecture notes are designed to be self-contained. However, a list of suggested references for optional supplementary reading will be provided in the lecture notes.

Staff Feedback

StStudents will be given feedback (through both verbal and written comments) in the following forms in this course:

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

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

Information listed in this course summary is tentative, and students will be made aware of any alterations to all aspects of the course e.g., general announcements, changes in

assessment availability and due dates, information regarding the structure of the intensive week, via the course Wattle site.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Pre-intensive period (4 weeks remote; 15 January - 9 February; each week consists of 3 x 1-1.5 hours of pre-recorded lectures and 1 x 2.5 hours of pre-recorded tutorials) C1: Revision of Linear Models C2: ANOVA and ANCOVA C3: Binary Logistic Regression C4: Poisson Log-Linear Regression C5: Introduction to Generalised Linear Models (GLMs)	Assignment 1 open and due
2	Intensive period (1 week in hybrid format; 12 February - 16 February; each day consists of 3.5 hours of in-person (on campus) lectures and 2.5 hours of in-person (on campus) tutorials; both tutorials and lectures will be streamed live and recorded): C6: Inference and Variable Selection for GLMs C7: Model Diagnostics C8: Binomial Logistic Regression C9: Multicategory Logistic Regression C10: Over and Under-Dispersion
3	Post-intensive period (4 weeks remote; 19 February - 15 March; there are no classes during this period; lecturer and tutor are available for consultation over Zoom)	Assignment 2 open and dueTake-home exam open and due

Tutorial Registration

Tutorial registration is not required.

Assessment Summary

Assessment task	Value	Due Date	Return of assessment	Learning Outcomes
Assignment 1	20 %	09/02/2024	23/02/2024	1,2,3
Assignment 2	30 %	01/03/2024	15/03/2024	1,2,3
Final Exam	50 %	15/03/2024	14/04/2024	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
• Special Assessment Consideration Guideline and General Information 
• 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

• During the four week pre-intensive period, there will be 3 x 1-1.5 hour of pre-recorded lectures, plus 1 x 2.5 hour of pre-recorded tutorials per week.
• During the one-week intensive period, the course will operate in a hybrid format: students will have the option (but are not required) to attend on-campus lectures and tutorials in-person. Each day of the intensive week will consists of 1 x 3.5 hour live (on-campus) lectures in the morning, and 1 x 2 hour live (on-campus) tutorials in the afternoon. All lectures and tutorials will be streamed live via Zoom, as well as recorded.
• There will be no classes during the post-intensive period.
• All assessment tasks will be conducted remotely.

Examination(s)

The course involves a take-home exam, which is tentatively scheduled to take place in the last week of the course i.e., late post-intensive week 4. You will be required to sign a declaration, in the form of a cover sheet, as part of the submission of your solutions to the take-home exam.

Assessment Task 1

Assignment 1

The assignment will assess up to Chapters 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 basic of generalized linear models. This assignment is expected to involve a combination of mathematical/conceptual questions (50%) and data analysis in R (50%).

Availability: By 5:00 pm Canberra time, Friday 26 January

Due date: Friday 9 February at 5:00 pm Canberra Time

Value: 20%

Assessment Task 2

Assignment 2

The assignment will assess Chapters 5-10 of the course. It must be completed individually, and is not redeemable. This assignment is designed to help you understand and reinforce many of the elements of understanding and applying generalized linear models models, and is expected to involve a combination of mathematical/conceptual questions (50%) and data analysis in R (50%).

Availability: By 5:00 pm Canberra time, Friday 16 February

Due date: Friday 1 March at 5:00 pm Canberra Time.

Value: 30%

Assessment Task 3

Final Exam

Details of the final take-home exam will be announced on Wattle no later than the end of the intensive week i.e., by 5pm Canberra time on 16 Februrary. It must be completed individually, and is not redeemable. The exam will assess all chapters 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.

Due date: The take-home exam is tentatively scheduled to take place around or on Friday 15 March.

Value: 50%

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 your assignment/s. 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 will be investigated under the University’s Academic Misconduct Rule.

Each assignment and the finnal take-home exam must be submitted as one or more electronic files, preferably a combination of pdf, potentially accompanied by appropriate R software scripts, to the

appropriate activity on the course Wattle site. 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 site, in due course.

Hardcopy Submission

There is no hardcopy submission required in this course.

Late Submission

No submission of any assessment tasks without an approved extension after the due date is permitted. If an assessment task is submitted after the due date, then unless an extension has been approved, 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.

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 Access and inclusion 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 and Learning Centre supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents undergraduate and ANU College students
• PARSA supports and represents postgraduate and research students