- Class Number 5754
- Term Code 3360
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Tao Zou
- Dr Tao Zou
- 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
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.
Upon successful completion, students will have the knowledge and skills to:
- Explain in detail the role of generalised linear modelling techniques (GLMs) in modern applied statistics and implement methodology.
- Demonstrate an in-depth understanding of the underlying assumptions for GLMs and perform diagnostic checks whilst identifying potential problems.
- Perform detailed statistical analyses using statistical software, incorporating underlying theory and methodologies.
Where possible, topics will be related to current research problems and reflect real world situations to emphasize the use of the techniques covered.
Additional Course Costs
The only other additional course costs are a calculator and printing materials.
Examination Material or equipment
Non-programmable scientific calculator.
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.
As there is 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 provide a list of suggested references for optional supplementary reading in the lecture notes.
Students 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.
Students will also be given written comments in the marked assignments.
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.
|Week/Session||Summary of Activities||Assessment|
|1||Introduction and Revision of Linear Models|
|2||ANOVA and ANCOVA|
|3||Linear Mixed Effects Models||Assignment 1 open|
|4||Introduction to Generalised Linear Models (GLMs)|
|5||Binary Logistic Regression||Assignment 1 due|
|6||Inference and Variable Selection for GLMs|
|7||Poisson Log-Linear Regression|
|9||Binomial Logistic Regression||Assignment 2 open|
|10||Multicategory Logistic Regression|
|11||Over and Under-Dispersion||Assignment 2 due|
|12||Various Topics of Interest, e.g., Odds Ratios and Contingency Tables, GLM Neural Networks, etc.|
Tutorial registration 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 task||Value||Due Date||Return of assessment||Learning Outcomes|
|Assignment 1||10 %||23/08/2023||31/08/2023||1,2,3|
|Assignment 2||25 %||18/10/2023||27/10/2022||1,2,3|
|Final Exam||65 %||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:
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 weekly on-campus lectures (recorded and available via echo360 on Wattle) and weekly tutorials, all delivered on campus. Weekly consultations with the lecturer and the tutor(s) will be conducted over Zoom.
Centrally scheduled examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information.
Assessment Task 1
Learning Outcomes: 1,2,3
Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials of Weeks 1-4. Assignments will include both derivation and application problems. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced in class and on Wattle. Assignments are expected to be in a PDF or Word file.
Value: 10% and compulsory.
Estimated return date: The week after submission.
Assessment Task 2
Learning Outcomes: 1,2,3
Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials of Weeks 1-10. Assignments will include both derivation and application problems. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced in class and on Wattle. Assignments are expected to be in a PDF or Word file.
Value: 25% and compulsory.
Estimated return date: The week after submission.
Assessment Task 3
Learning Outcomes: 1,2,3
The final examination is worth 65% of the final raw score. Examination materials and conditions will be notified to all students via Wattle no later than Week 10 of the semester. The exam will be held on campus. In addition, the exam will be centrally timetabled, and details of the final examination timetable will be made available on the ANU Timetabling website.
Value: 65% and compulsory.
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.
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. You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.
There is no hardcopy submission in the course.
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.
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.
The marked assignments will be returned 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
It will not be possible for assignments to be resubmitted.
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 all ANU students
Covariance regression modelling, network data modelling, financial statistics, environmental statistics, dependent data analysis and big data analysis
Dr Tao Zou