Requirements to Pass this Unit

To pass this unit you must:

• Achieve a total mark equal to or greater than 50%.

Hurdle Assessments

There is no Hurdle Assessment.

Attendance and participation

We strongly encourage all students to actively participate in all learning activities. Regular engagement is crucial for your success in this unit, as these activities provide opportunities to deepen your understanding of the material, collaborate with peers, and receive valuable feedback from instructors, to assist in completing the unit assessments. Your active participation not only enhances your own learning experience but also contributes to a vibrant and dynamic learning environment for everyone.

Late Assessment Submission Penalty

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of 0 will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is provided to students who experience a technical concern.

For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.

Assessments where Late Submissions will be accepted.

In this unit late submissions will be accepted as follows:

• Assignment – YES, Standard Late Penalty applies;
• Case study/analysis – YES, Standard Late Penalty applies.
• Final Exam – NO, unless Special Consideration is granted.

Special Consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the written assessments in this unit on time, please inform the convenor and submit a Special Consideration request through http://connect.mq.edu.au.

Written Assessments/Quizzes/Tests: If you experience circumstances or events that affect your ability to complete the written assessments in this unit on time, please inform the convenor and submit a Special Consideration request through https://connect.mq.edu.au.

Classes

Lectures (beginning in Week 1): There is one two-hour lectures each week.

SGTA classes (beginning in Week 2): Students must register for one one-hour class per week.

The timetable for classes can be found on the University website at: https://publish.mq.edu.au

Enrolment can be managed using eStudent at: https://students.mq.edu.au/support/technology/systems/estudent

Suggested textbooks

The following book is useful as supplementary resources, for additional questions and explanations. 

1. James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: With applications in R (2nd ed.). Springer.

2. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.

3. Kuhn, M., & Silge, J. (2022). Tidy modeling with R. O’Reilly Media, Inc.

Technology Used and Required

This subject requires the use of the following computer software:

• R: R is a free statistical software package. Access and installation instructions may be found at: https://www.r-project.org/
• RStudio: RStudio is an open source tool that is used to manage and present work performed using R. Access and installation instructions may be found at https://rstudio.com/products/rstudio/download/
• LaTeX: LaTeX is a free mathematical typesetting program. You should use this to help to typeset your assignment. Access and installation instructions may be found at: https://www.latex-project.org/get/
• Quarto: An open-source scientific and technical publishing system, insatlled by default with the latest release of RStudio. 

Communication

We will communicate with you via your university email or through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion forum or sent to your lecturers from your university email address.

COVID Information

For the latest information on the University’s response to COVID-19, please refer to the Coron- avirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn.

This is a draft schedule and is subjected to change.

Student feedback from the previous offering of this unit was very positive, with students appreciated the clarity of assessment requirements, the availability of support, and the overall structure and delivery of the unit. Many also greatly valued the open-ended case study, noting it provided a meaningful opportunity to practise independent analysis and apply statistical reasoning to real-world problems. Although some students initially found the case study challenging, they acknowledged its value in developing skills for stakeholder communication and in navigating uncertainty or ambiguity in data analysis.

Given the strong feedback, no changes have been made to the delivery of the unit for this offering. However, we remain committed to continuously improving the level of support and student engagement, and we welcome further feedback as the session progresses.