Students

STAT3191 – Statistical Inference for Data Science

2026 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Georgy Sofronov
georgy.sofronov@mq.edu.au
Contact via Email
12WW 529
See iLearn for consultation hours
Lecturer
Hugh Entwistle
hugh.entwistle@mq.edu.au
Contact via Email
See iLearn for consultation hours
Credit points Credit points
10
Prerequisites Prerequisites
STAT2170 and COMP2200
Corequisites Corequisites
Co-badged status Co-badged status
STAT6191
Unit description Unit description

Statistical inference allows us to draw meaningful conclusions about a population by analysing a representative sample. This unit covers foundational probability concepts, providing the framework for using sample data to make inferences about the broader population. It then explores the theory and application of classical statistical inference techniques to quantify uncertainty and make informed decisions. The unit also introduces the Bayesian approach, which combines prior knowledge with sample data for a more holistic, subjective analysis, especially useful in areas with limited data or significant prior knowledge. The focus is on building a strong conceptual understanding, with practical examples to reinforce theory and highlight real-world relevance.

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: Explain and use Probability Theory relevant to Statistical Inference.
  • ULO2: Demonstrate knowledge of the fundamental principles of inference.
  • ULO3: Estimate key population parameters of interest, test hypotheses about them and construct confidence regions.
  • ULO4: Evaluate and use likelihood-based inference methods.
  • ULO5: Evaluate and use elementary Bayesian inference methods.
  • ULO6: Analyse data using computing packages which implement the most common Inference procedures.

General Assessment Information

To pass this unit you must achieve a total mark equal to or greater than 50%.

There are no hurdle assessments for this unit.  

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 of the task) will be applied for each day a written report or presentation 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. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience technical concerns. For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, please apply for Special Consideration. For example, if the assignment is worth 8 marks (of the entire unit) and your submission is late by 19 hours (or 23 hours 59 minutes 59 seconds), 0.4 marks (5% of 8 marks) will be deducted. If your submission is late by 24 hours (or 47 hours 59 minutes 59 seconds), 0.8 marks (10% of 8 marks) will be deducted, and so on.

Assessments where Late Submissions will be Accepted 

  • Statistical Inference Problem Set – YES, Late Assessment Submission Penalty applies.
  • Project Report – YES, Late Assessment Submission Penalty applies. This assessment is NOT eligible for a Short Extension.
  • Final Exam – NO, unless Special Consideration is granted.

Release

  • Statistical Inference Problem Set: To be released no later than 28th August.
  • Project Report: Specifications to be released no later than 18th September.

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 http://connect.mq.edu.au/.

Short Extensions

For some assessment types, students are allowed to request an extra 3 calendar days to complete eligible assessments. No reason or evidence is required. 

Assessments where Short Extensions Apply 

  • Statistical Inference Problem Set – YES
  • Project Report – NO
  • Final Exam – NO

Final Exam Policy

It is Macquarie University policy not to set early examinations for individuals or groups of students. All students are expected to ensure that they are available until the end of the teaching semester, that is, the final day of the official examination period. The only excuse for not sitting an examination at the designated time is because of documented illness or unavoidable disruption. In these special circumstances, you may apply for special consideration via https://connect.mq.edu.au.

If you receive special consideration for the final exam, a supplementary exam will be scheduled in the interval between the regular exam period and the start of the next session. By making a special consideration application for the final exam you are declaring yourself available for a resit during this supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Statistical Inference Problem Set 20% No 11/09/2026 Individual Yes Open
Final Exam 50% No Exam Period Individual No Observed
Project Report 30% No 23/10/2026 Group No Open

Statistical Inference Problem Set

Assessment Type 1: Problem-based task
Indicative Time on Task 2: 15 hours
Due: 11/09/2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

Students will be given a set of problems to complete on their own as a take-home assessment. In this assessment, students will reinforce and apply the concepts covered in lectures, along with the skills developed in SGTA sessions.
On successful completion you will be able to:
  • Explain and use Probability Theory relevant to Statistical Inference.
  • Demonstrate knowledge of the fundamental principles of inference.
  • Estimate key population parameters of interest, test hypotheses about them and construct confidence regions.

Final Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 25 hours
Due: Exam Period
Weighting: 50%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

You will undertake a final examination during the formal examination period.
On successful completion you will be able to:
  • Explain and use Probability Theory relevant to Statistical Inference.
  • Demonstrate knowledge of the fundamental principles of inference.
  • Estimate key population parameters of interest, test hypotheses about them and construct confidence regions.
  • Evaluate and use likelihood-based inference methods.
  • Evaluate and use elementary Bayesian inference methods.
  • Analyse data using computing packages which implement the most common Inference procedures.

Project Report

Assessment Type 1: Written Submission
Indicative Time on Task 2: 30 hours
Due: 23/10/2026
Weighting: 30%
Groupwork/Individual: Group
Short extension 3: No
AI Approach: Open

A written report must be submitted, in which students will demonstrate their practical skills by applying statistical techniques to a simulation-based inference problem.
On successful completion you will be able to:
  • Explain and use Probability Theory relevant to Statistical Inference.
  • Demonstrate knowledge of the fundamental principles of inference.
  • Estimate key population parameters of interest, test hypotheses about them and construct confidence regions.
  • Evaluate and use likelihood-based inference methods.
  • Evaluate and use elementary Bayesian inference methods.
  • Analyse data using computing packages which implement the most common Inference procedures.

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
  • Academic Success 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

Lectures (beginning in Week 1): A one-hour lecture each week.

SGTA classes (beginning in Week 2): A two-hour SGTA class each week. Students must register for the SGTA class.

Students can use the Class Finder tool in eStudent to see when and where classes are being held via Publish: https://publish.mq.edu.au/.

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

Computing and Software

R, RStudio, and Quarto are freely available for download and will be used in the SGTA sessions and assessment tasks for this unit.

Recommended References:

  • Wasserman, Larry. All of Statistics: A Concise Course in Statistical Inference. Springer Texts in Statistics, Springer New York, 2004.
  • Casella, George, and Roger L. Berger. Statistical Inference. 2nd ed., Duxbury, 2002.

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

Week 1: Introduction to statistical inference; fundamental concepts of probability; basic set theory.

Week 2: Random variables; discrete and continuous probability distributions; joint, marginal and conditional probabilties; independence.

Week 3: Common probability distributions; expectations and other key moments.

Week 4: Sequences of random variables; modes of convergence.

Week 5: Statistical models and estimation; sampling; properties of estimators; introductory estimation methods.

Week 6: Introduction to likelihood; key likelihood concepts.

Week 7: Maximum likelihood estimation (MLE); computation, properties and inference with MLE.

Week 8: Additional properties of estimators; miminum variance estimators; confidence intervals.

Week 9: Standard hypothesis testing.

Week 10: Likelihood-based hypothesis testing.

Week 11: The Bayesian paradigm; Bayes' theorem; Bayesian inference.

Week 12: Prior Specification; conjugate priors; maximum posteriori estimates; credible intervals.

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 https://students.mq.edu.au/support/technology/service-desk. 

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

We value student feedback to be able to continually improve the way we offer our units. As such we encourage students to provide constructive feedback via student surveys, to the teaching staff directly, or via the FSE Student Experience & Feedback link in the iLearn page.

Unit information based on version 2026.02 of the Handbook