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Unit of study_

BSTA5210: Regression Modelling for Biostatistics 1 (RM1)

Semester 1, 2024 [Online] - Camperdown/Darlington, Sydney

The aim of this unit is to lay the foundation of biostatistical modelling to analyse data from randomised or observational studies. These skills are essential for biostatistics in practice and will be used by students for the remainder of their Master of Biostatistics studies. This unit will introduce the motivation for different regression analyses and how to choose an appropriate modelling strategy. This unit will teach how to use linear regression to analyse continuous outcomes and logistic regression for binary outcomes. Emphasis will be placed on interpretation of results and checking the model assumptions. Stata and R software will be used to apply the methods to real study datasets.

Unit details and rules

Unit code BSTA5210
Academic unit Public Health
Credit points 6
BSTA5007 or BSTA5008
(BSTA5011 or PUBH5010 or CEPI5100)
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Armando Teixeira-Pinto,
Lecturer(s) Armando Teixeira-Pinto,
Farzaneh Boroumand,
Type Description Weight Due Length
Assignment Assignment 1
Statistical analysis and written report
30% Week 05
Due date: 25 Mar 2024 at 23:59
8-10 pages
Outcomes assessed: LO1 LO2 LO4 LO5
Assignment Assignment 2
Statistical analysis and written report
30% Week 10
Due date: 06 May 2024 at 23:59
8-10 pages
Outcomes assessed: LO1 LO2 LO4 LO5
Assignment Assignment 3
Statistical analysis and written report
40% Week 12
Due date: 26 May 2024 at 23:59
10-12 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5

Assessment summary

  • Assignment 1 is a written report covering topics up to Week 4
  • Assignment 2 is a written report covering topics up to Week 8
  • Assignment 3 is a writtern report covering all unit topics
  • Assignments will typically require students to perform statistical anslysis followed by a written report, and may also require mathematical deriviations of statistical principles. 

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result code

Result name

Mark range



High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard.



75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard.



65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard.



50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard.



0 - 49

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


Absent fail

0 - 49

When you haven’t completed all assessment tasks or met the attendance requirements.

For more information see guide to grades.

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:

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 weekends and public holidays).

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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 There will be an online live tutorial and discussion held fortnightly across the semester (W1, 3, 5, 7, 9 and 11). The schedule will be decided in consultation with the students. Tutorial (6 hr) LO1 LO2 LO3 LO4 LO5
Week 01 Simple linear regression Individual study (10 hr) LO1 LO2 LO4 LO5
Week 02 Checking assumptions in linear regression Individual study (10 hr) LO1 LO2 LO4 LO5
Week 03 Binary covariates, outliers and influential observations Individual study (10 hr) LO1 LO2 LO4 LO5
Week 04 Multiple linear regression application Individual study (10 hr) LO1 LO2 LO4 LO5
Week 05 Multiple linear regression theory Individual study (10 hr) LO1 LO2 LO4 LO5
Week 06 Interaction and collinearity Individual study (10 hr) LO1 LO2 LO4 LO5
Week 07 Assumption violations Individual study (10 hr) LO1 LO2 LO4 LO5
Week 08 Q+A Tutorial (1 hr) LO1 LO2 LO4 LO5
Linear regression model building Individual study (10 hr) LO1 LO2 LO4 LO5
Week 09 Logistic regression Individual study (10 hr) LO1 LO3 LO4 LO5
Week 10 Confounding and interaction in logistic regression Individual study (10 hr) LO3 LO4 LO5
Week 11 Checking assumptions in logistic regression Individual study (10 hr) LO3 LO4 LO5
Week 12 Logistic regression model building Individual study (10 hr) LO3 LO4 LO5
Q+A Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5

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

Textbook: Vittinghoff E, Glidden D, Shiboski, S. McCulloch C. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (2nd edition). Springer, 2012. 

An online copy of this text can be accessed for free via the University of Sydney Library.

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. Explain the motivations for different regression analyses and be able to select and apply a suitable modelling approach based on the research aim
  • LO2. Analyse data using normal linear models, and be able to assess model fit and diagnostics
  • LO3. Analyse data using logistic regression models for binary data, and be able to assess model fit and diagnostics
  • LO4. Accurately interpret and manipulate mathematical equations that relate to regression analysis
  • LO5. Effectively communicate the outcomes and justification of a regression analysis

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

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

This unit was first delivered in 2022 based on an external program review and feedback from other Master of Biostatistics core units. The students' feedback has generally been very positive with only minor changes implemented between semesters.

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

Software requirements: You can use either Stata or R in this unit. The notes assume the use of Stata v12 or later. The notes for this course show both R and Stata code whenever possible. The textbook for this unit shows Stata code only, and so relevant equivalent code for R is provided in the course notes.


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

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