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Unit of study_

MATH1905: Statistical Thinking with Data (Advanced)

This unit is designed to provide a thorough preparation for further study in mathematics and statistics. It is a core unit of study providing three of the twelve credit points required by the Faculty of Science as well as a foundations requirement in the Faculty of Engineering. This Advanced level unit of study parallels the normal unit MATH1005 but goes more deeply into the subject matter and requires more mathematical sophistication.


Academic unit Mathematics and Statistics Academic Operations
Unit code MATH1905
Unit name Statistical Thinking with Data (Advanced)
Session, year
Semester 2, 2022
Attendance mode Normal day
Location Remote
Credit points 3

Enrolment rules

MATH1005 or MATH1015 or STAT1021 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901
Assumed knowledge

HSC Mathematics Extension 2 or 90 or above in HSC Mathematics Extension 1 or equivalent

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Uri Keich,
Lecturer(s) Uri Keich ,
Administrative staff Mathematics and Statistics Student Services Office, Carslaw 520
Type Description Weight Due Length
Final exam (Record+) Type B final exam Exam
Written and MCQ
60% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Online task Quiz 1
15% Week 04
Due date: 26 Aug 2022 at 23:59

Closing date: 26 Aug 2022
40 minutes
Outcomes assessed: LO2 LO3 LO4
Assignment Assignment 1
Written assignment
10% Week 09
Due date: 07 Oct 2022 at 23:59

Closing date: 17 Oct 2022
10 days
Outcomes assessed: LO2 LO3 LO4 LO5 LO6 LO8
Online task Quiz 2
15% Week 11
Due date: 21 Oct 2022 at 23:59

Closing date: 21 Oct 2022
40 minutes
Outcomes assessed: LO6 LO8
Type B final exam = Type B final exam ?
  • Examination: There is one examination during the examination period at the end of Semester 2. Further information about the exam will be made available at a later date on the website. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator. 
  • Quizzes: quizzes will be held online through Canvas. The quizzes are 40 minutes and have to be submitted by the closing time of 23:59 on the due date. The quiz can be taken any time during the 24 hour period before the closing time. The better mark principle will be used for the quiz so do not submit an application for Special Consideration or Special Arrangements if you miss a quiz. The better mark principle means that the quiz counts if and only if it is better than or equal to your exam mark. If your quiz mark is less than your exam mark, the exam mark will be used for that portion of your assessment instead.
  • Assignments: There is one assignment, which must be submitted electronically, as PDF files only, in Turnitin, via Canvas by the deadline. Note that your assignment will not be marked if it is illegible or if it is submitted sideways or upside down. It is your responsibility to check that your assignment has been submitted correctly (check that you can view each page). Late submisions will receive a penalty. A mark of zero will be awarded for all submissions more than 10 days past the original due date. Further extensions past this time will not be permitted.
  • Simple extensions: No simple extensions are given in first year units in the School of Mathematics and Statistics.

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

Representing complete or close to complete mastery of the material.


75 - 84

Representing excellence, but substantially less than complete mastery.


65 - 74

Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.


50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and
important ideas and concepts in the course.


0 - 49

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

For more information see

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

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.

WK Topic Learning activity Learning outcomes
Week 01 Intro/Chapters 1 and 2 Lecture (2 hr) LO1 LO2 LO8
Week 02 Chapters 3 and 4 Lecture (2 hr) LO2 LO8
Tutorial 1 Tutorial (1 hr) LO2 LO8
Week 03 Chapters 5 and 6 Lecture (2 hr) LO2 LO3 LO8
Tutorial 2 Tutorial (1 hr) LO2 LO3 LO8
Week 04 Chapters 8 and 9 Lecture (2 hr) LO2 LO4 LO8
Tutorial 3 Tutorial (1 hr) LO2 LO3 LO4 LO8
Week 05 Chapters 10 and 11 Lecture (2 hr) LO2 LO3 LO4 LO8
Tutorial 4 Tutorial (1 hr) LO2 LO3 LO4 LO8
Week 06 Chapter 12 and geometry of regression Lecture (2 hr) LO2 LO3 LO4 LO8
Tutorial 5 Tutorial (1 hr) LO2 LO3 LO4 LO8
Week 07 Chapter 13, 14, 15 and 16 Lecture (2 hr) LO5 LO6 LO8
Tutorial 6 Tutorial (1 hr) LO5 LO6 LO8
Week 08 Chapters 17 and 18 Lecture (2 hr) LO3 LO5 LO6
Tutorial 7 Tutorial (1 hr) LO3 LO5 LO6
Week 09 Central Limit Theorem Lecture (2 hr) LO3 LO5
Tutorial 8 Tutorial (1 hr) LO3 LO5
Week 10 Chapters 20 and 21 Lecture (2 hr) LO3 LO6
Tutorial 9 Tutorial (1 hr) LO3 LO6
Week 11 Chapters 23 and 26 Lecture (2 hr) LO6 LO7 LO8
Tutorial 10 Tutorial (1 hr) LO6 LO7 LO8
Week 12 Chapters 28 and 29 Lecture (2 hr) LO6 LO7 LO8
Tutorial 11 Tutorial (1 hr) LO6 LO7 LO8
Week 13 Revision Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Tutorial 12 Tutorial (1 hr) LO6 LO7 LO8

Attendance and class requirements

Due to the exceptional circumstances caused by the COVID-19 pandemic, attendance requirements for this unit of study have been amended. Where on-campus or online tutorials/workshops/laboratories have been scheduled, students should make every effort to attend and participate at the scheduled time. If you are unable to attend for any reason (e.g. health or technical issues) you should attend another session, if available. Penalties will not apply if you cannot attend your scheduled class.

  • Attendance: Unless otherwise indicated, students are expected to attend a minimum of 80% of timetabled activities for a unit of study, unless granted exemption by the Associate Dean.
  • Tutorial attendance: You should attend the tutorial given on your personal timetable. Attendance at tutorials will be recorded. Your attendance will not be recorded unless you attend the tutorials in which you are enrolled.  While there is no penalty if 80% attendance is not met we strongly recommend you attend tutorials regularly to keep up with the material and to engage with the tutorial questions. Since there is no assessment associated with the tutorials do not submit an application for Special Consideration or Special Arrangements for missed tutorials. Tutorials start in week 2.

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 3 credit point unit, this equates to roughly 60-75 hours of student effort in total.

Required readings

  • Textbook: Statistics (4th Edition) – Freedman, Pisani, and Purves (2007)

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 difference between a randomised controlled experiment and an observational study, in particular the limitations of the latter
  • LO2. Produce and interpret various graphical and numerical summaries of univariate and bivariate data
  • LO3. Determine when and how to use the normal curve to approximate frequencies and probabilities
  • LO4. Determine when and how to use least-squares regression and correlation to describe a bivariate relationship
  • LO5. Show mathematically why binomial probability histograms are approximately normal
  • LO6. Use a simple statistical model (“box model”) to explain the random behaviour of sample sums and means
  • LO7. Use box models as the basis for various statistical tests
  • LO8. Apply the methods learnt to various real-world examples and draw sensible, practical statistical conclusions from them

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
Minor changes were made to the weightings for the assignments and final exam.


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