Outline

Overview

Mathematics and Statistics provide powerful quantitative tools to solve problems and make informed decisions in a very diverse range of real-life applications. This unit builds on the calculus that you learnt in Mathematics 1A and introduces you to mathematical statistics. Mathematics 1B gives you a foundational knowledge of the theory of multivariable calculus, differential equations and mathematical statistics that will underpin examples of applications in this unit and in other areas that you will study. At the end of this unit, you will be equipped with the theory and tools that you need to use mathematics and statistics for mathematical and statistical modelling and problem solving. You will also be prepared to continue your studies in mathematics, statistics and financial mathematics and statistics at this university. Please note that this unit is not part of the Data Science major. Students are very strongly recommended to complete MATH1061 Mathematics 1A before starting MATH1062 Mathematics 1B.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	MATH1905 or MATH1903 or MATH1907 or MATH1923 or MATH1933 or MATH1972 or MATH1962 or MATH1003 or MATH1023 or MATH1005 or MATH1015
Assumed knowledge ?	Knowledge of complex numbers and methods of differential and integral calculus including integration by partial fractions and integration by parts as for example in MATH1021 or MATH1921 or MATH1931 or MATH1061 or HSC Mathematics Extension 2
Available to study abroad and exchange students	No

Teaching staff

Coordinator	John Mitry, john.mitry@sydney.edu.au
Lecturer(s)	Yeeka Yau, yeeka.yau@sydney.edu.au
	James Morgan, james.morgan@sydney.edu.au
	Thomas Elton, telt8898@uni.sydney.edu.au
	John Mitry, john.mitry@sydney.edu.au
	Tom Goertzen, tom.goertzen@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Multiple choice and written calculations	60%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Weekly Online Quizzes (Calculus) Weekly online quizzes 3-10 (Calculus)	3%	Multiple weeks	1 hour per week	AI allowed
Outcomes assessed:
Out-of-class quiz	Weekly Online Quizzes (Statistics) Weekly R coding Ed Lessons 3-10 (Statistics)	3%	Multiple weeks	1 hour per week	AI allowed
Outcomes assessed:
Out-of-class quiz	Weekly Online Quizzes (Calculus) (Early Feedback Task) Weekly online quizzes 1-2 (Calculus)	1%	Week 03 Due date: 23 Aug 2026 at 23:59 Closing date: 23 Aug 2026	1 hour per quiz	AI allowed
Outcomes assessed:
Out-of-class quiz	Weekly Online Quizzes (Statistics) (Early Feedback Task) Weekly R coding Ed Lessons 1-2 (Statistics)	1%	Week 03 Due date: 23 Aug 2026 at 23:59 Closing date: 23 Aug 2026	1 hour per quiz	AI allowed
Outcomes assessed:
Written work	Assignment 1 Written calculations, computational data analysis, reflective analysis	5%	Week 04 Due date: 30 Aug 2026 at 23:59 Closing date: 09 Sep 2026	2-4 pages	AI allowed
Outcomes assessed:
In-person written or creative task	In-person Quiz Multiple choice	15%	Week 07	40 minutes	AI prohibited
Outcomes assessed:
Written work	Assignment 2 Written calculations, computational data analysis, reflective analysis	10%	Week 10 Due date: 18 Oct 2026 at 23:59 Closing date: 28 Oct 2026	4-6 pages	AI allowed
Outcomes assessed:
Contribution	Contribution Contribution to tutorials and workshops	2%	Weekly	2x50 minutes per week	AI allowed
Outcomes assessed:
= early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Assignments: There are two assignments (each with calculus and statistics component). Each component must be submitted electronically as one single typeset or scanned PDF file via Canvas by the deadline. Assignments that are illegible, incorrectly oriented or unreadable files cannot be marked. It is your responsibility to ensure that your submission is complete and viewable (please check that all pages have uploaded correctly).
We recognise that unexpected circumstances can arise, and there are processes in place to support you if you need extra time. Simple Extensions of up to 5 calendar days are available. If your circumstances affect you for more than 5 days, you have the option of applying for Special Consideration. Assignments submitted after the due date without an approved extension will incur a late penalty. Submissions cannot be accepted more than 10 calendar days after the original due date. This allows for timely release of marks and feedback. If your circumstances affect you for longer than 10 days, the outcome for an approved Special Consideration application will be a mark adjustment using your mark for the other assignment. If you are granted a mark adjustment for both assignments, then your assignment marks will both be zero. If you are granted a mark adjustment for an assignment, but you choose to submit the assignment, then your mark for this submission will not count towards your final mark unless you withdraw your Special Consideration before assignment marks are released.

In-person Quiz: One in-person quiz will be held on campus during Week 7. You must sit the quiz at the time and location that appears as Assessment in your timetable. If you are unable to sit the quiz at that time for a valid reason, then you have the option to apply for Special Consideration or Special Arrangements. There will be one opportunity to sit a replacement quiz for students with approved Special Consideration. If the Special Consideration is approved too late to be able to sit the replacement quiz, then the outcome will be a mark adjustment using the final exam mark. Quiz feedback will be returned through Canvas.

Weekly Online Quizzes/R coding Ed Lessons: There are ten weekly online quizzes for calculus and ten weekly R coding Ed Lessons for statistics (through Canvas/Ed and equally weighted) and the marks for the best eight quizzes and best eight R coding Ed Lessons count towards your final grade. The first two quizzes and first two R coding Ed Lessons are used for the Early Feedback Task. This scheme already allows for a small number of non-attempts. You should only apply for Special Consideration if you are affected for at least 3 quizzes or at least 3 Ed lessons. Each online quiz for Calculus consists of a set of randomized questions. The outcome for approved Special Consideration will be a mark adjustment using your marks for the other weekly online quizzes. The deadline for completion of each quiz and R coding Ed Lesson is 23:59 Sunday (starting in week 3). The precise schedule for the quizzes and R coding Ed Lessons is found on Canvas and the Ed discussion forum. We recommend that you follow the due dates outlined on Canvas and Ed to gain the most benefit from these quizzes and R coding Ed Lessons.

Contribution: Tutorial and workshop contribution is assessed on a satisfactory / non-satisfactory basis and reflects your engagement in tutorial and workshop activities. It is worth 0.125 marks per tutorial or workshop class, up to 8 Calculus tutorials (out of 11) and 8 Statistics workshops (out of 12). This scheme already allows for a small number of absences. You should only apply for Special Consideration if you are affected for several tutorials/workshops (for example, more than 3–4 in a stream). Special Consideration is not required for occasional missed classes. The outcome for approved Special Consideration will be a mark adjustment using contribution marks for non-affected tutorials and workshops.

Final Exam: The final exam for this unit is compulsory and must be attempted. Failure to attempt the final exam will result in an AF grade for the unit. Further information about the exam will be made available at a later date on Canvas. If a second replacement exam is required, this exam may be delivered via an alternative secure assessment method, such as a viva voce (in-person 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.

Detailed information for each assessment can be found on Canvas.

Even though the use of AI is allowed for some assessments, it is better for your learning to do your own work to complete your assessments.

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	Description
High distinction	85 - 100	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

5% of assessment weight per day for assignments.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to mathematical modelling and differential equations; graphical summaries	Lecture (3 hr)
Week 02	Separable equations and examples; numerical summaries	Lecture (3 hr)
	Introduction to mathematical modelling and differential equations	Tutorial (1 hr)
	Graphical summaries	Workshop (1 hr)
Week 03	Applications of separable equations; understanding chance and variability	Lecture (3 hr)
	Separable equations and examples	Tutorial (1 hr)
	Numerical summaries	Workshop (1 hr)
Week 04	Linear differential equations; understanding chance and variability	Lecture (3 hr)
	Applications of separable equations	Tutorial (1 hr)
	Understanding chance and variability	Workshop (1 hr)
Week 05	Second order linear differential equations; the normal model, the central limit theorem, confidence intervals	Lecture (3 hr)
	Linear differential equations	Tutorial (1 hr)
	Understanding chance and variability	Workshop (1 hr)
Week 06	Inhomogeneous and linear systems of differential equations; test for a proportion	Lecture (3 hr)
	Second order linear differential equations	Tutorial (1 hr)
	The normal model, the central limit theorem, confidence intervals	Workshop (1 hr)
Week 07	Curves and surfaces in 3D; test for a mean	Lecture (3 hr)
	Inhomogeneous and linear systems of differential equations	Tutorial (1 hr)
	Test for a proportion	Workshop (1 hr)
Week 08	Partial derivatives, tangent planes, differentials; test for a difference of two proportions	Lecture (3 hr)
	Curves and surfaces in 3D	Tutorial (1 hr)
	Test for a mean	Workshop (1 hr)
Week 09	Directional derivatives and chain rule; linear models	Lecture (3 hr)
	Partial derivatives, tangent planes, differentials	Tutorial (1 hr)
	Test for a difference of two proportions	Workshop (1 hr)
Week 10	Implicit differentiation and gradient vectors; linear models	Lecture (3 hr)
	Directional derivatives and chain rule	Tutorial (1 hr)
	Linear models	Workshop (1 hr)
Week 11	Higher-order derivatives; tests for relationships	Lecture (3 hr)
	Implicit differentiation and gradient vectors	Tutorial (1 hr)
	Linear models	Workshop (1 hr)
Week 12	Optimisation of functions of two variables; chi-squared tests and p-values	Lecture (3 hr)
	Higher-order derivatives	Tutorial (1 hr)
	Tests for relationships	Workshop (1 hr)
Week 13	Revision	Lecture (3 hr)
	Optimisation of functions of two variables	Tutorial (1 hr)
	Chi-squared tests and p-values	Workshop (1 hr)

Attendance and class requirements

Lecture attendance: You are expected to attend lectures. If you do not attend lectures you should follow the lecture recordings or livestream available through Canvas.

Tutorial/workshop attendance: Tutorials and workshops (two per week) start in Week 2. You should attend the tutorial and workshop given on your personal timetable. Attendance at tutorials and workshops and contribution will be recorded to determine the contribution mark. We strongly recommend you attend tutorials and workshops regularly to keep up with the material and to engage with the tutorial/workshop questions.

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

See the Canvas site for reference material.

Learning outcomes

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.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. apply mathematical logic and statistical thinking to solve problems
LO2. express mathematical and statistical ideas and arguments coherently in written form
LO3. identify appropriate methods to describe, summarise and visualise a given data set
LO4. identify and apply appropriate methods of inference for a variety of data types
LO5. apply statistical software such as R to analyse example sets of data
LO6. express surfaces and curves in three dimensions as equations in Cartesian coordinates and interpret functions of two variables as surfaces in three-dimensional Cartesian space
LO7. calculate partial derivatives of functions of several variables and use these to find directional derivatives and gradient vectors and to interpret the physical and geometric significance of these quantities
LO8. create differential equations models and use a variety of techniques to solve these differential equations and interpret their solutions in terms of the original problem
LO9. apply concepts of mathematical statistics and calculus to a variety of contexts and applications

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
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

In statistics, in response to feedback in the Unit of Study Survey, R coding is now practiced via Weekly R coding Ed Lessons.

Additional information

Lectures: Lectures are face-to-face and streamed live with online access from Canvas.

Tutorials: Tutorials are small classes in which you are expected to work through questions from the tutorial sheet in small groups on the white board. The role of the tutor is to provide support and to some extent give feedback on your solutions written on the board.

Workshops: Workshops are small classes in which you are expected to work through questions from the tutorial sheet.

Tutorial and exercise sheets: The question sheets for a given week will be available on the MATH1062 Canvas page. Solutions to tutorial exercises for week n will usually be posted on the web by the afternoon of the Friday of week n.

Ed Discussion forum: https://edstem.org

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

MATH1062: Mathematics 1B

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links