BSTA5100: Semester 1, 2025
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Unit outline_

BSTA5100: Mathematics Foundations of Biostatistics (MFB)

Semester 1, 2025 [Online] - Camperdown/Darlington, Sydney

This unit aims to develop and apply calculus and other mathematically-based techniques to the study of probability and statistical distributions. This unit covers the foundational mathematical methods and probability distribution concepts necessary for an in depth understanding of biostatistical methods. The unit commences with an introduction to mathematical expressions, followed by the fundamental calculus techniques of differentiation and integration, and essential elements of matrix algebra. The concepts and rules of probability are then introduced, followed by the application of the calculus methods covered earlier in the unit to calculate fundamental quantities of probability distributions, such as mean and variance. Random variables, their meaning and use in biostatistical applications is presented, together with the role of numerical simulation as a tool to demonstrate the properties of random variables.

Unit details and rules

Academic unit Public Health
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
BSTA5023
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Erin Cvejic, erin.cvejic@sydney.edu.au
The census date for this unit availability is 31 March 2025
Type Description Weight Due Length
Assignment AI Allowed Assignment 4
Written assignment covering all Module content
35% Formal exam period
Due date: 16 Jun 2025 at 23:59
2000 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Tutorial quiz AI Allowed Online quizzes
Online MCQ and short-answers
0% Multiple weeks Online during modules
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment AI Allowed Assignment 1
Written assignment covering Module 0 and 1
15% Week 04
Due date: 30 Mar 2025 at 23:59
1000 words
Outcomes assessed: LO1 LO2
Assignment AI Allowed Assignment 2
Written assignment covering Module 2 and 3
35% Week 07
Due date: 20 Apr 2025 at 23:59
2000 words
Outcomes assessed: LO1 LO4 LO3 LO2
Assignment AI Allowed Assignment 3
Written assignment covering Module 4 and 5
15% Week 10
Due date: 18 May 2025 at 23:59
1000 words
Outcomes assessed: LO1 LO6 LO5 LO4
AI allowed = AI allowed ?

Assessment summary

  • Assignment 1 covers content from Module 0 and Module 1
  • Assignment 2 covers content from Module 2 and Module 3
  • Assignment 3 covers content from Module 4 and Module 5
  • Assignment 4 covers content from Modules 1-7  

There are also non-assessed online quizzes available for various modules. 

Additional information for all assessments will be provided on Canvas.

The assessment tasks in this Unit have been designed to be challenging, authentic and complex. Although individual assessment components may provide specific guidance regarding the use of generative artificial intelligence (AI) tools (e.g., ChatGPT), successful completion of these components will require students to critically engage in specific contexts and tasks for which AI will provide only limited support and guidance. In all cases, a failure to reference the use of generative AI may constitute student misconduct under the Student Code of Conduct of your University of enrolment. To successfully complete assessment tasks, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools. The exception is if there are particular assessment tasks which designate the use of generative AI, in which case details will be provided in the assignment instructions.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

When you meet the learning outcomes of the unit at an exceptional standard.

Distinction

75 - 84

When you meet the learning outcomes of the unit at a very high standard.

Credit

65 - 74

When you meet the learning outcomes of the unit at a good standard.

Pass

50 - 64

When you meet the learning outcomes of the unit at an acceptable standard.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

Absent Fail 0-49 When you don’t meet the learning outcomes of the unit to a satisfactory standard through non-submission of assessable item(s).

For more information see sydney.edu.au/students/guide-to-grades

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator. 

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions. 

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit. 

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply. 

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The standard BCA policy for late penalties for submitted work is a 5% deduction from the earned mark for each day the assessment is late, up to a maximum of 10 days (including public holidays and weekends).

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Multiple weeks Module 1: Limits and derivatives Independent study (20 hr) LO1 LO2
Module 2: Integration, multi-dimensional functions and numerical approximations Independent study (20 hr) LO1 LO2
Module 4: Probability concepts Independent study (10 hr) LO1 LO4 LO5
Module 5: Discrete random variables Independent study (20 hr) LO1 LO4 LO5 LO6
Module 6: Continuous random variables Independent study (20 hr) LO1 LO2 LO4 LO5 LO6 LO7
Module 7: Multiple random variables Independent study (20 hr) LO1 LO2 LO4 LO5 LO6 LO7
Week 01 Module 0: Introduction - Numbers and Functions Independent study (10 hr) LO1
Week 06 Module 3: Matrices and determinants Independent study (10 hr) LO1 LO2 LO3

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

  • Class notes will be provided for the the mathematical concepts components of the unit.
  • For the probability and distributions components, the prescribed textbook is: Wackerley DD, Mendenhall W, Schaeffer RL. Mathematical Statistics with Applications. 7th edition. 2008 Thomson Learning, Inc. (Duxbury, Thomson Brooks/Cole) ISBN-13: 978-0-495-11081-1
  • This textbook is central to this component of the unit and you must have unrestricted access to this book.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Manipulate general mathematical expressions and inequalities
  • LO2. Understand and apply the essential elements of calculus, including differentiation and integration
  • LO3. Manipulate and evaluate matrix expressions and calculate inverses of matrices
  • LO4. Demonstrate an understanding of the meaning and laws of probability
  • LO5. Recognise common probability distributions and their properties
  • LO6. Apply calculus-based tools to derive key features of a probability distribution and properties of random variables, such as mean and variance
  • LO7. Demonstrate skills in simulation of random variables to illustrate and explain statistical concepts

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

This unit of study was last offered in Semester 2, 2024. Minor errors in notes have been corrected. Existing exercise solutions have been extended and clarified and comprehensive solutions are provided for all exercises.

This unit is externally delivered as part of the Biostatistics Collaboration of Australia (BCA).

Students will need access to either Stata (version 15 or higher) or R (version 4.0.0 or later), or both. In addition, you may also want to use WolframAlpha for learning the mathematics component and for checking calculus derivations.  WolframAlpha runs in a web page so there is no software to obtain or download. No prior experience with any of R, Stata, or WolframAlpha is required or assumed. 

Required mathematical background:
Because this unit is an introductory foundational unit, the only background that is required is high school mathematics, and in particular the concepts of functions, and being comfortable solving equations and manipulating fractions. Material will be presented starting at an elementary level, but the pace is rapid and some brushing up from previous studies will be advantageous, particularly for topics listed as being covered in Module 0.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

This unit of study outline was last modified on 03 Feb 2025.

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