Course coordinator
Dr Simon Hart
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- The Environment School
Our understanding of biology is uncertain because biological systems are subject to stochasticity, and because our ability to quantitatively observe biological systems is imperfect. Statistical modelling is the approach that allows us to 'peer through' (and quantify) this uncertainty to understand how biological systems work. Therefore, the goal of this course is to provide you with a solid foundation in statistical modelling in a biological context. We will cover some basic probability (the 'language' of uncertainty) in the context of methods of estimation (ordinary least squares and maximum likelihood) and we will then begin with a deep dive into simple linear regression models assuming Gaussian errors and including both metric and nominal predictor variables. We will then build on this foundation by learning how to fit statistical models to data with non-Gaussian errors via so-called generalized linear models (GLMs). Next we will learn methods to account for correlation/non-independence in data by including so-called random effects in our statistical models (i.e. we will learn mixed/multilevel/hierarchical modelling). Finally, we will introduce an alternative statistical philosophy and modelling approach based on Bayesian (rather than Frequentist) statistical methods. The course will be very applied, providing lots of opportunities to learn by doing. An important focus of the course will be to develop an intuition for the iterative process of statistical modelling from question or hypothesis through data exploration, model fitting, model diagnostics, model selection, and visualization, interpretation and presentation of results. Indeed, much of the labyrinthine world of statistical modelling can be navigated by carefully implementing a relatively consistent modelling process. Once you become comfortable with the process, you can problem solve the details for the rest of your career in quantitative biology.
Welcome to QBIO7005! This course will help you to unlock the secrets of the living world using statistical modelling. Statistical modelling is an essential tool in the contemporary Quantitative Biologist's toolkit, enabling you to strip away noise and uncertainty to identify the processes driving our biological world. Throughout this course you will develop the practical knowledge and confidence to build your own statistical models according to dominant inferential philosophies using widely-used statistical software. At the conclusion of this course you will be well placed to confidently apply appropriate data analytical techniques for solving many of the basic and applied biological problems you will encounter in your career. Just as importantly, you will be able to use this course as a foundation for continuing to deepen and extend your understanding of statistical modelling long into the future. We (Simon and Simone) very much look forward to providing you with a solid foundation in statistical modelling.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
QBIOL7001
Jointly taught details
This course is jointly-taught with:
- Another instance of the same course
QBIO7005 is taught both internally and externally.
Course contact
Course staff
Lecturer
Dr Simone Blomberg
Dr Simon Hart
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The aim of QBIO7005 is to provide students with the ability to make inferences from data about basic and applied biological problems using frequentist and Bayesian analytical approaches implemented in statistical software that is widely used in the workplace.ﾠ
ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Understand the goals of statistical modelling.
LO2.
Build, evaluate, interpret, and present the results of statistical models with continuous and categorical independent variables and normal error structures (i.e. linear regression, including multiple regression).
LO3.
Build, evaluate, interpret, and present the results of statistical models with continuous and categorical independent variables and non-normal error structures (i.e. generalized linear models [GLMs]).
LO4.
Build, evaluate, interpret, and present the results of statistical models for data that are correlated/non-independent (i.e. mixed/multilevel/hierarchical models)
LO5.
Implement analyses in the software package R.
LO6.
Develop problem solving and critical thinking skills, as well as analytical agility the ability to rapidly and confidently move between biological problems and datasets requiring different analytical approaches.
LO7.
Communicate analytical methods and results
LO8.
Understand the differences between dominant philosophies of statistical inference Frequentist and Bayesian.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Project | Multiple Regression | 25% |
30/03/2026 2:00 pm |
| Project |
Oral Presentations
|
50% 25% each |
1) Generalised Linear Model: 24/04/2026 2) Bayesian Analyses: 29/05/2026
Note: Please see Blackboard for more information on your scheduled time for the presentations, these will occur outside of the regularly scheduled classes |
| Project | Multilevel Models | 25% |
11/05/2026 2:00 pm |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Multiple Regression
- Mode
- Written
- Category
- Project
- Weight
- 25%
- Due date
30/03/2026 2:00 pm
- Learning outcomes
- L01, L02, L05, L06, L07
Task description
Students will be provided with a biological problem and a dataset. Students will be required to:
a) build an appropriate statistical model,
b) implement the model in R,
c) evaluate and refit the model as required,
d) present and interpret the results of the final model, and
e) present evidence of using appropriate tools for model evaluation and interpretation.
The project will be submitted as a short report with a description of the background and scientific question, analytical methods, analytical results (including figures), and a brief written interpretation and conclusion based on the analytical results.
Project will be based on building, implementing (in R), evaluating, and presenting the results of an appropriate statistical model.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Online submission by Turnitin only by the due date and time. Refer to Blackboard for the submission link. No hard copy or assignment cover sheets are required. Submission via email is not accepted.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
See the Additional Assessment Information section below for information relating to extension applications.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item, then 10% of the maximum possible mark for the assessment item (the assessment ‘marked from’ value) will be deducted as a late penalty for every day (or part day) late after the due date.
For example, if you submit your assignment 1 hour late, you will be penalised 10%; if your assignment is 24.5 hours late, you will be penalised 20% (because it is late by one 24-hour period plus part of another 24-hour period).
Oral Presentations
- Hurdle
- Identity Verified
- Mode
- Oral
- Category
- Project
- Weight
- 50% 25% each
- Due date
1) Generalised Linear Model: 24/04/2026
2) Bayesian Analyses: 29/05/2026
Note: Please see Blackboard for more information on your scheduled time for the presentations, these will occur outside of the regularly scheduled classes
- Learning outcomes
- L01, L03, L05, L06, L07
Task description
Generalised Linear Model 25%
Students will be provided with a biological problem and a dataset. Students will be required to:
a) build an appropriate statistical model,
b) implement the model in R,
c) evaluate and refit the model as required,
d) interpret the results of the final model, and
e) present evidence of using appropriate tools for model evaluation and interpretation.
The project will be assessed via an oral presentation, followed by questions from assessors.
Bayesian Analyses 25%
Students will be provided with a biological problem and a dataset. Students will be required to:
a) build an appropriate statistical model,
b) implement the model in R,
c) evaluate and refit the model as required,
d) interpret the results of the final model, and
e) present evidence of using appropriate tools for model evaluation and interpretation.
The project will be assessed via an oral presentation, followed by questions from assessors.
While the Oral Presentation of these assessments is done in person, students may to assist in preparing the assessments, use Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
See Additional Course Grading Information for the hurdle information relating to this assessment item.Submission guidelines
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
See the Additional Assessment Information section below for information relating to extension applications.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item, then 10% of the maximum possible mark for the assessment item (the assessment ‘marked from’ value) will be deducted as a late penalty for every day (or part day) late after the due date.
For example, if you submit your assignment 1 hour late, you will be penalised 10%; if your assignment is 24.5 hours late, you will be penalised 20% (because it is late by one 24-hour period plus part of another 24-hour period).
Multilevel Models
- Mode
- Written
- Category
- Project
- Weight
- 25%
- Due date
11/05/2026 2:00 pm
- Learning outcomes
- L01, L03, L04, L05, L06, L07
Task description
Students will be provided with a biological problem and a dataset. Students will be required to:
a) build an appropriate statistical model,
b) implement the model in R,
c) evaluate and refit the model as required,
d) present and interpret the results of the final model, and
e) present evidence of using appropriate tools for model evaluation and interpretation.
The project will be submitted as a short report with a description of the background and scientific question, analytical methods, analytical results (including figures), and a brief written interpretation and conclusion based on the analytical results.
Project will be based on building, implementing (in R), evaluating, and presenting the results of an appropriate statistical model.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Online submission by Turnitin only by the due date and time. Refer to Blackboard for the submission link. No hard copy or assignment cover sheets are required. Submission via email is not accepted.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
See the Additional Assessment Information section below for information relating to extension applications.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item, then 10% of the maximum possible mark for the assessment item (the assessment ‘marked from’ value) will be deducted as a late penalty for every day (or part day) late after the due date.
For example, if you submit your assignment 1 hour late, you will be penalised 10%; if your assignment is 24.5 hours late, you will be penalised 20% (because it is late by one 24-hour period plus part of another 24-hour period).
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 0% |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 30% |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: The minimum percentage required for this grade is: 45% |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 50% |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 65% |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 75% |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is 85% |
Additional course grading information
In order to pass this course, you must meet the following requirements (if you do not meet these requirements, the maximum grade you will receive will be a 3):
You must obtain 50% or more on the Oral Presentations Assessment sequence
Supplementary assessment
Supplementary assessment is available for this course.
Should you fail a course with a grade of 3, you may be eligible for supplementary assessment.
Refer to the link above for information on supplementary assessment and how to apply. Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.
If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met. Supplementary assessment can take any form (such as a written report, oral presentation, examination or other appropriate assessment) and may test specific learning outcomes tailored to the individual student, or all learning outcomes.
To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.
Additional assessment information
Applications for Extensions to Assessment Due Dates
Read the information contained in the following links carefully before submitting an application for extension to assessment due date.
For guidance on applying for an extension, information is available here: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension
For the policy relating to extensions, information is available here (Part D): https://policies.uq.edu.au/document/view-current.php?id=184
Please note the University's requirements for medical certificates here: https://my.uq.edu.au/information-and-services/manage-my-program/uq-policies-and-rules/requirements-medical-certificates
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (23 Feb - 01 Mar) |
Problem-based learning |
Simple linear regression Lecturer: Simon Hart Learning outcomes: L01, L02, L05, L06, L07, L08 |
Week 2 (02 Mar - 08 Mar) |
Problem-based learning |
Simple and multiple linear regression Lecturer: Simon Hart Learning outcomes: L01, L02, L05, L06, L07 |
Week 3 (09 Mar - 15 Mar) |
Problem-based learning |
Linear & generalized linear models (GLMs) Lecturer: Simon Hart Learning outcomes: L01, L03, L05, L06, L07 |
Week 4 (16 Mar - 22 Mar) |
Problem-based learning |
Linear & generalized linear models (GLMs) Lecturer: Simon Hart Learning outcomes: L01, L03, L05, L06, L07 |
Week 5 (23 Mar - 29 Mar) |
Problem-based learning |
Linear & generalized linear models (GLMs) Lecturer: Simon Hart Learning outcomes: L01, L03, L05, L06, L07 |
Week 7 (13 Apr - 19 Apr) |
Problem-based learning |
Multilevel models Lecturer: Simone Blomberg Learning outcomes: L01, L04, L05, L06, L07 |
Week 8 (20 Apr - 26 Apr) |
Problem-based learning |
Multilevel models Lecturer: Simone Blomberg Learning outcomes: L01, L04, L05, L06, L07 |
Week 9 (27 Apr - 03 May) |
Problem-based learning |
Multilevel models Lecturer: Simone Blomberg Learning outcomes: L01, L04, L05, L06, L07 |
Week 10 (04 May - 10 May) |
Problem-based learning |
Bayesian methods Lecturer: Simone Blomberg Learning outcomes: L01, L05, L06, L07, L08 |
Week 11 (11 May - 17 May) |
Problem-based learning |
Bayesian methods Lecturer: Simone Blomberg Learning outcomes: L01, L05, L06, L07, L08 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
- AI for Assessment Guide
- Recording of Teaching Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.