Students

STAT2170 – Statistical Data Analytics

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Jun Han
Contact via email
See iLearn for consultation hours.
Credit points Credit points
10
Prerequisites Prerequisites
FOSE1015 or STAT1170 or STAT1371 or STAT1250 or STAT1103
Corequisites Corequisites
Co-badged status Co-badged status
STAT6180
Unit description Unit description

Building upon the statistical foundation laid in the 1000-level statistics unit, STAT2170 aims to provide an extension, emphasising the practical application of data analytics to a diverse range of real-world problems. In this unit, students will acquire the skills necessary to conduct and interpret various statistical analyses and tests, including one-way and two-way analysis of variance, as well as multiple linear regression. The primary focus is on hands-on applications using the R programming language, enabling students to engage in the end-to-end data science workflow. Students will utilise R to visualise data, construct models, and validate their results, ensuring a comprehensive understanding of statistical data analysis in practical contexts.

Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Create specific questions tailored to the context or domain and determine the suitable statistical methods for analysis.
  • ULO2: Create, assess, and interpret numerical and visual summaries for various data types, including extensive or intricate datasets.
  • ULO3: Integrate a software version control system, such as Git and Github, into the analysis workflow.
  • ULO4: Identify, justify and implement appropriate parametric or non-parametric statistical tests
  • ULO5: Formulate, validate, evaluate and interpret appropriate linear models to describe the correlations among multiple factors.
  • ULO6: Create a reproducible report to communicate statistical insights using a literate programming tool, such as Quarto.

General Assessment Information

Attendance and participation

We strongly encourage all students to participate actively 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.

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 in this unit.

Late Submission Policy

  • 5% penalty per day: If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days.

    • Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.

    • Example 2 (out of 30): If you score 27/30 but submit 1 day late, you will lose 1.5 marks and receive 25.5/30.

  • After 7 days: Submissions more than 7 days late will receive a mark of 0.

  • Extensions:

    • Automatic short extension: Some assessments are eligible for automatic short extension. You can only apply for an automatic short extension before the due date.

    • Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration.

Need help? Review the Special Consideration page HERE.

Assessments where Late Submissions will be accepted:

  • R-based Skill Assessment – YES, Standard Late Penalty applies;
  • Case Study – YES, Standard Late Penalty applies;
  • Final Examination – 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 assessments in this unit on time, please inform the convenor and submit a Special Consideration request through https://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.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI assisted?
R-based skill assessment 25% No 27/03/2026 Individual No
Case study 35% No 18/05/2026 Individual No Open AI
Final Examination 40% No Exam Period Individual No Observed

R-based skill assessment

Assessment Type 1: Experiential task
Indicative Time on Task 2: 11 hours
Due: 27/03/2026
Weighting: 25%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?:

You will demonstrate your practical data analysis skills by applying statistical techniques to an assigned dataset during an invigilated, in-class R-based practice test.


On successful completion you will be able to:
  • Create specific questions tailored to the context or domain and determine the suitable statistical methods for analysis.
  • Create, assess, and interpret numerical and visual summaries for various data types, including extensive or intricate datasets.
  • Integrate a software version control system, such as Git and Github, into the analysis workflow.
  • Identify, justify and implement appropriate parametric or non-parametric statistical tests

Case study

Assessment Type 1: Problem-based task
Indicative Time on Task 2: 20 hours
Due: 18/05/2026
Weighting: 35%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?: Open

You will conduct a case study using a real-world dataset to apply the concepts and techniques learned in this unit. You will demonstrate your analytical and communication skills by presenting your findings in a clear, reproducible report.


On successful completion you will be able to:
  • Create specific questions tailored to the context or domain and determine the suitable statistical methods for analysis.
  • Create, assess, and interpret numerical and visual summaries for various data types, including extensive or intricate datasets.
  • Integrate a software version control system, such as Git and Github, into the analysis workflow.
  • Identify, justify and implement appropriate parametric or non-parametric statistical tests
  • Formulate, validate, evaluate and interpret appropriate linear models to describe the correlations among multiple factors.
  • Create a reproducible report to communicate statistical insights using a literate programming tool, such as Quarto.

Final Examination

Assessment Type 1: Examination
Indicative Time on Task 2: 10 hours
Due: Exam Period
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?: Observed

You will complete a formal invigilated examination to demonstrate your achievement of the unit’s learning outcomes by recalling, applying, and evaluating key concepts.


On successful completion you will be able to:
  • Create specific questions tailored to the context or domain and determine the suitable statistical methods for analysis.
  • Create, assess, and interpret numerical and visual summaries for various data types, including extensive or intricate datasets.
  • Identify, justify and implement appropriate parametric or non-parametric statistical tests
  • Formulate, validate, evaluate and interpret appropriate linear models to describe the correlations among multiple factors.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

Classes

Workshops (beginning in Week 1): There is a one-hour workshop each week.

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

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

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

Suggested textbooks

The following textbook is useful as a supplementary resource for additional questions and explanations. It is available from the Macquarie University library:

  • Moore, D.S., 2017. Introduction to the Practice of Statistics. WH Freeman and Company.

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/
  • Quarto: An open-source scientific and technical publishing system, installed by default with the latest release of RStudio.
  • LaTeX: LaTeX is a free mathematical typesetting program. Access and installation instructions may be found at: https://www.latex-project.org/get/

Methods of Communication

We will communicate with you via your university email and through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to the unit convenor via the contact email on iLearn.

Unit Schedule

This is a draft schedule and is subject to change.

Week Topics Assessment
1 Course introduction; One-sided tests; Type I and Type II error; Introduction to R/RStudio  
2 Modified two-sample t-test; Assessing normality and equal variance assumptions  
3 One-way ANOVA  
4 One-way ANOVA, Multiple comparisons   
5 Transformations; Non-parametrics; Power and sample size R-Based Skill Assessment Due
6 Data management; R Markdown; Simple linear regression  
Session 1 Break    
7 Simple linear regression and model validation; Multiple regression  
8

Multiple regression and model validation

 
9 Extensions and examples of multiple regression  
10 Two-way ANOVA Case Study Due
11 Two-Way ANOVA and multiple comparisons  
12 Two-Way ANOVA, regression, and multiple comparisons  
13 Exam Details and Revision  

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes from Previous Offering

The previous 2-hour lecture has been replaced with pre-recorded lecture videos and a 1-hour workshop. The videos introduce the core statistical concepts, and you are expected to review them before class. The workshop will focus on worked examples, problem-solving, and questions to help you apply the material and prepare for the assessments.

To enable students more time to focus on learning, understanding, and reflecting on the content of our unit, we have revised the assessment structure as follows. There are now only three assessments: a skills assessment, a case report, and a final exam. Although no marks are associated with attendance, all activities provide you with key content designed to help you understand the content and complete the assessments.

 

Changes since First Published

Date Description
10/02/2026 Changes to the staff contact list.

Unit information based on version 2026.02 of the Handbook