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Unit of study_

ECMT3160: Statistical Modelling

Semester 1, 2021 [Normal day] - Remote

This unit provides an accessible foundation in the principles of probability and mathematical statistics that underlie econometric methodologies. Its objective is to develop a deeper understanding of econometric methodologies encountered in intermediate units of study. The unit starts with the notion of a simple random experiment and extends it to the notion of datagenerating process for observational data in econometrics. The core topics of this unit include distribution finding techniques, identification in parametric and semiparametric models, estimation theory, and hypothesis testing, Monte Carlo techniques and the bootstrap.

Unit details and rules

Unit code ECMT3160
Academic unit Economics
Credit points 6
ECMT3620 or ECMT3720 or ECMT3210
ECMT2150 or ECMT2950 or ECMT2110 or ECMT2010
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Rami Tabri,
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final exam
written take-home examination during formal exam period.
50% Formal exam period 2 hours
Outcomes assessed: LO5 LO1 LO2 LO3 LO4
Assignment Assignment 1
10% Week 06
Due date: 16 Apr 2021 at 15:00

Closing date: 23 Apr 2021
500 words
Outcomes assessed: LO1 LO2 LO3
In-semester test (Open book) Type C in-semester exam Mid-semester test
Take-home exam
30% Week 08
Due date: 30 Apr 2021 at 15:00
2 hours
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment 2
10% Week 12
Due date: 28 May 2021 at 18:00

Closing date: 04 Jun 2021
500 words
Outcomes assessed: LO4 LO5 LO3 LO2 LO1
Type C final exam = Type C final exam ?
Type C in-semester exam = Type C in-semester exam ?

Assessment summary

Late take-home exams will not be accpeted.

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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

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

For more information see

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.

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.

WK Topic Learning activity Learning outcomes
Week 01 Discussion of course; review of sets and operations, and notion of a function; basic discussion of probability. Lecture and tutorial (3 hr) LO1
Week 02 Rules of Probability: notions of an experiment, event, addition rules Lecture and tutorial (3 hr) LO1
Week 03 Probability Rules: addition, conditioning, and statistical independence. Lecture and tutorial (3 hr) LO1 LO2
Week 04 Counting and Combinatorics Lecture and tutorial (3 hr) LO3
Week 06 Counting and Combinatorics Lecture and tutorial (3 hr) LO3
Week 07 Distributions, trials and approximation Lecture and tutorial (3 hr) LO4
Week 09 Distributions, trials and approximation Lecture and tutorial (3 hr) LO4
Week 10 Random variables and their distributions Lecture and tutorial (3 hr) LO4
Week 11 Random Variables and their distributions Lecture and tutorial (3 hr) LO4
Week 12 Jointly Distributed Random Variables Lecture and tutorial (3 hr) LO5
Week 13 Jointly Distributed Random Variables Lecture and tutorial (3 hr) LO5

Attendance and class requirements

  • Attendance: According to Faculty Board Resolutions, students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board. The Examiner’s Board will decide whether you should pass or fail the unit of study if your attendance falls below this threshold.
  • Lecture recording: Most lectures (in recording-equipped venues) will be recorded and may be made available to students on the LMS. However, you should not rely on lecture recording to substitute your classroom learning experience.
  • Preparation: Students should commit to spend approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

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

All readings for this unit can be accessed on the Library link available on Canvas.

Required textbook: Probability and Random Variables: A Beginner’s Guide, by David Stirzaker.

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. understand basic ideas and methods of probability
  • LO2. understand notions of statistical independence and conditioning
  • LO3. understand counting methods and other combinatorics, and ways of using them.
  • LO4. understand notions of random variable and probability distribution, and ways of using them.
  • LO5. understand notions of random vectors and their distributions, weak law of large numbers, and central limit theorem, and their use in statistical approximation.

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.

No changes have been made since this unit was last offered.


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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