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Course profile

Statistical Modelling in Biology (QBIO7005)

Study period
Sem 1 2025
Location
St Lucia
Attendance mode
In Person

Course overview

Study period
Semester 1, 2025 (24/02/2025 - 21/06/2025)
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

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

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%

24/03/2025 2:00 pm

Project Generalised Linear Model 25%

17/04/2025 2:00 pm

Project Multilevel Models 25%

12/05/2025 2:00 pm

Project Bayesian Analyses 25%

30/05/2025 2:00 pm

Assessment details

Multiple Regression

Mode
Written
Category
Project
Weight
25%
Due date

24/03/2025 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. No hard copy or assignment cover sheets required.

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 further below for information relating to extension and deferral application.

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 (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). 

Generalised Linear Model

Mode
Written
Category
Project
Weight
25%
Due date

17/04/2025 2:00 pm

Learning outcomes
L01, L03, 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. No hard copy or assignment cover sheets required.

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 further below for information relating to extension and deferral 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 (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

12/05/2025 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. No hard copy or assignment cover sheets required.

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 further below for information relating to extension and deferral 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 (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). 

Bayesian Analyses

Mode
Written
Category
Project
Weight
25%
Due date

30/05/2025 2:00 pm

Learning outcomes
L01, L04, L05, L06, L07, L08

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. No hard copy or assignment cover sheets required.

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 further below for information relating to extension and deferral 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 (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%

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 my.UQ 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

Assessment Submission

It is the responsibility of the student to ensure the on time, correct and complete submission of all assessment items.

Please ensure you receive and save the submission confirmation for all submitted items, you may be asked to produce this as evidence of your submission.

Applications for Extensions to Assessment Due Dates

Extension requests are submitted online via my.UQ – applying for an extension. Extension requests received in any other way will not be approved. Additional details associated with extension requests, including acceptable and unacceptable reasons, may be found at my.UQ.

Please note:

  • Requests for an extension to an assessment due date must be submitted through your my.UQ portal and you must provide documentation of your circumstances, as soon as it becomes evident that an extension is needed. Your application must be submitted on or before the assessment item's due date and time.
  • Applications for extension can take time to be processed so you should continue to work on your assessment item while awaiting a decision. We recommend that you submit any completed work by the due date, and this will be marked if your application is not approved. Should your application be approved, then you will be able to resubmit by the agreed revised due date.
  • If an extension is approved, you will be notified via your my.UQ portal and the new date and time for submission provided. It is important that you check the revised date as it may differ from the date that you requested.
  • If the basis of the application is a medical condition, applications should be accompanied by a medical certificate dated prior to the assignment due date. If you are unable to provide documentation to support your application by the due date and time you must still submit your application on time and attach a written statement (Word document) outlining why you cannot provide the documentation. You must then upload the documentation to the portal within 24 hours.
  • If an extension is being sought on the basis of exceptional circumstances, it must be accompanied by supporting documentation (eg. Statutory declaration).
  • For extensions based on a SAP you may be granted a maximum of 7 days (if no earlier maximum timeframe applies). See the Extension or Deferral availability section of each assessment for timeframes. Your SAP is all that is required as documentation to support your application. However, additional extension requests for the assessment item will require the submission of additional supporting documentation e.g., a medical certificate. All extension requests must be received by the assessment due date and time.
  • An extension for an assessment item due within the teaching period in which the course is offered, must not exceed four weeks in total. If you are incapacitated for a period exceeding four weeks of the teaching period, you are advised to apply for Removal of Course.
  • If you have been ill or unable to attend class for more than 4 weeks, you are advised to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty - seek advice from the Faculty that administers your program.
  • Students may be asked to submit evidence of work completed to date. Lack of adequate progress on your assessment item may result in an extension being denied.
  • There are no provisions for exemption from an assessment item within UQ rules. If you are unable to submit an assessment piece then, under special circumstances, you may be granted an exemption, but may be required to submit alternative assessment to ensure all learning outcomes are met.

Turnitin

All written assessment must be submitted via the appropriate Turnitin submission portal, which can be found within the Blackboard site. You are responsible for ensuring that your submission is complete. It is wise to re-enter the Turnitin portal and confirm that your submission is there and that it has not been altered during the submission process.

By submitting work through Turnitin you are deemed to have accepted the following declaration “I certify that this assignment is my own work and has not been submitted, either previously or concurrently, in whole or in part, to this University or any other educational institution, for marking or assessment”.

In the case of a Blackboard outage, please contact the Course Coordinator as soon as possible to confirm the outage with ITS.

Assessment/Attendance

Please notify your Course Coordinator as soon as you become aware of any issue that may affect your ability to meet the assessment/attendance requirements of the course. The my.UQ website and the Course Profile for your course also provide information about your course requirements, the rules associated with your courses and services offered by the University.

A note for repeating students in this course

Any student who enrols in a course must not be given exemption or partial credit from their previous attempt(s) for any individual piece of assessment. Instead, the student must successfully complete all of the learning activities and assessment items within the study period of enrolment (PPL Assessment - Procedures).

If the same assessment item is set from one year to the next, repeating students are allowed to submit the same work they submitted in previous attempts at the course. Where possible SENV recommends that you use the feedback you received in your last attempt to improve parts of the item where you lost marks. Resubmission of an altered or unaltered assessment item by a repeating student (where the same assessment has been set) will not be considered as self-plagiarism.

Plagiarism

You should be aware that the University employs purpose built software to detect plagiarism. It is very important that you understand clearly the practical meaning of plagiarism.

DEFINITION OF PLAGIARISM: Plagiarism is the act of misrepresenting as one's own original work the ideas, interpretations, words or creative works of another. These include published and unpublished documents, designs, music, sounds, images, photographs, computer codes and ideas gained through working in a group. These ideas, interpretations, words or works may be found in print and/or electronic media.

EXAMPLES OF PLAGIARISM:

1. Direct copying of paragraphs, sentences, a single sentence or significant parts of a sentence;

2. Direct copying of paragraphs, sentences, a single sentence or significant parts of a sentence with an end reference but without quotation marks around the copied text;

3. Copying ideas, concepts, research results, computer codes, statistical tables, designs, images, sounds or text or any combination of these;

4. Paraphrasing, summarising or simply rearranging another person's words, ideas, etc without changing the basic structure and/or meaning of the text;

5. Offering an idea or interpretation that is not one's own without identifying whose idea or interpretation it is;

6. A 'cut and paste' of statements from multiple sources;

7. Presenting as independent, work done in collaboration with others;

8. Copying or adapting another student's original work into a submitted assessment item.

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.

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Learning period Activity type Topic
Week 1

(24 Feb - 02 Mar)

Problem-based learning

Simple linear regression

Lecturer: Simon Hart

Learning outcomes: L01, L02, L05, L06, L07, L08

Week 2

(03 Mar - 09 Mar)

Problem-based learning

Simple and multiple linear regression

Lecturer: Simon Hart

Learning outcomes: L01, L02, L05, L06, L07

Week 3

(10 Mar - 16 Mar)

Problem-based learning

Linear & generalized linear models (GLMs)

Lecturer: Simon Hart

Learning outcomes: L01, L03, L05, L06, L07

Week 4

(17 Mar - 23 Mar)

Problem-based learning

Linear & generalized linear models (GLMs)

Lecturer: Simon Hart

Learning outcomes: L01, L03, L05, L06, L07

Week 5

(24 Mar - 30 Mar)

Problem-based learning

Linear & generalized linear models (GLMs)

Lecturer: Simon Hart

Learning outcomes: L01, L03, L05, L06, L07

Week 6

(31 Mar - 06 Apr)

Problem-based learning

Multilevel models

Lecturer: Simone Blomberg

Learning outcomes: L01, L04, L05, L06, L07

Week 7

(07 Apr - 13 Apr)

Problem-based learning

Multilevel models

Lecturer: Simone Blomberg

Learning outcomes: L01, L04, L05, L06, L07

Week 8

(14 Apr - 20 Apr)

Problem-based learning

Multilevel models

Lecturer: Simone Blomberg

Learning outcomes: L01, L04, L05, L06, L07

Week 10

(05 May - 11 May)

Problem-based learning

Bayesian methods

Lecturer: Simone Blomberg

Learning outcomes: L01, L05, L06, L07, L08

Week 11

(12 May - 18 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:

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.